henderson jj d 2016 final - virginia tech
TRANSCRIPT
“To Err on the Side of Caution:” Ethical Dimensions of the National Weather Service Warning Process
Jennifer J. Henderson
Dissertation submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements
for the degree of
Doctor of Philosophy in
Science and Technology Studies
Saul Halfon, Co-‐Chair Gary Lee Downey, Co-‐Chair
James H. Collier Sonja D. Schmid Rebecca E. Morss
Dec. 6, 2016 Blacksburg, VA
Keywords: National Weather Service, weather warnings, expertise,
accuracy, ethic of care
Copyright 2016, Jennifer J. Henderson
“To Err on the Side of Caution:” Ethical Dimensions of the National Weather Service Warning Process
Jennifer J. Henderson
ABSTRACT
This dissertation traces three ethical dimensions, or values, of weather warnings in the National Weather Service (NWS): an ethic of accuracy, and ethic of care, and an ethic of resilience. Each appear in forecaster work but are not equally visible in the identity of a forecaster as scientific expert. Thus, I propose that the NWS should consider rethinking its science through its relationship to multiple publics, creating what Sandra Harding calls a “strong objectivity.” To this end, I offer the concept of empathic accuracy as an ethic that reflects the interrelatedness of precision and care that already attend to forecasting work. First, I offer a genealogy of the ethic of accuracy as forecasters see it. Beginning in the 1960s, operational meteorologists mounted an ethic of accuracy through the “man-‐machine mix,” a concept that pointed to an identity of the forecasting scientist that required a demarcation between humans and technologies. It is continually troubled by the growing power of computer models to make predictions. Second, I provide an ethnographic account of the concern expressed by forecasters for their publics. I do so to demonstrate how an ethic of care exists alongside accuracy in their forecasting science, especially during times of crisis. I recreate the concern for others that their labor performs. It is an account that values emotion and is sensitive to context, showing what Virginia Held called the “self-‐and-‐other together” that partially constitutes a forecaster identity. Third, I critique the NWS Weather Ready Nation Roadmap and its emphasis on developing in the public an ethic of resilience. I argue that, as currently framed, this ethic and its instantiation in the initiative Impact Based Decision Support Services narrowly defines community to such an extent that it disappears the public. However, it also reveals other valences of resilience that have the potential to open up a space for an empathetic accuracy. Finally, I close with a co-‐authored article that explores my own commitment to an ethic of relationality in disaster work and the compromises that create tension in me as a scholar and critical participant in the weather community.
“To Err on the Side of Caution:” Ethical Dimensions of the National Weather Service Warning Process
Jennifer J. Henderson
GENERAL AUDIENCE ABSTRACT
Every year, weather disasters affect people’s lives. When tornadoes, flash floods, winter weather, and heat threaten communities, forecasters in the National Weather Service (NWS) have the responsibility to issue alerts, which are called warnings, to help keep people safe from harm. For decades, these professionals have used the best technologies they have—Doppler radar, satellites, and observation networks—to scan the skies for potential danger. And they have done so diligently and with great attention to making their forecasts and warnings as accurate as possible. Yet each year, as these weather phenomena pose risks to people in their local communities, accuracy of warnings is not enough to keep people safe. This dissertation contributes to such concerns. Rather than focus on specific technologies that might be improved, I explore the professional identity of the NWS forecaster and potential changes to their science that might help them meet their mission to protect life. I offer insight into how NWS forecasters have chosen to see themselves and their role in society, and why. Specifically, my goal is to explore ways that the agency’s focus on accuracy is unintentionally masking other values that are important to the professional practices and activities of the forecaster. To help make the complexity of their identities more apparent, I offer a new kind of ethic, an empathetic accuracy, that better reflects not just the attention forecasters give to correct predictions but predictions done with care and concern for the people they serve. I explore the history of the term accuracy to show why it is so important in their work; I show how the notion of care is already key to their jobs; and I critique current policies that may either diminish or enhance their relationships with people in the general public. I suggest that the agency should consider developing a better kind of science that accounts for this complex professional image of the forecaster as scientist and public servant. More importantly, my goal is to show that NWS forecasters have alternative roles they can engage with that are equally, if not more important, to the people whose lives they are committed to protecting.
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Dedication This dissertation is dedicated to my mom, Joyce Marie Andrus Henderson, who inspires me every day. She has continually shown me by example what it means to be good, caring, and thoughtful. She is the most ethically minded person I know.
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Acknowledgements I owe the best parts of this dissertation to so many people. Dean DePauw, for putting me on a path to explore ethical issues; Skip Fuhrman, for making the Ph.D. possible; Carol Sue Slusser for making it all bearable. Advisors Saul Halfon and Gary Lee Downey, for working so diligently to help me finish and finish well; Jim Collier, for normative insight; Sonja Schmid, for helping me see the multiplicities of risk; and Rebecca Morss, for keeping me honest about weather forecasting. All of the National Weather Service forecasters and staff I’ve been privileged to know, learn from, observe, and befriend. I can’t thank you enough for your camaraderie and trust. I hope that this dissertation resonates with you in some small way. Friends and colleagues in STS: Crystal Cook Marshall, Max Liboiron, Scott Knowles, Katrina Petersen, Phaedra Daipha, Vivian Choi, Kim Fortun, Adam Smith, Carol Davis, Sarv Lotfi, Melissa Hafner, Monique Durfour, Ashley Shew, William Davis, Keith Johnson, Josh Brinkman, Trevor Croker, and Sumitra Nair. Faculty members in STS at Virginia Tech, you are all such good people. Thank you for everything. My NCAR family: Rebecca Morss, Julie Demuth, Heather Lazrus, and Olya Wilhelmi—for showing me kindness and an alternative career to academia. Friends and colleagues in the weather community: Tom LeFebvre, for being so generous with your time and for sharing the many histories of forecasting with me. Dave Carroll, Kevin Myatt, and Chris White for letting me participate as a VT storm chaser and showing me why such efforts matter. Susan Jasko, Laura Myers, Vankita Brown—my Weather Warriors, for all your love and friendship. Eve Gruntfest, Jen Spinney, and Dan Nietfeld for always believing in me. Bill Hooke, for seeing the possibilities in me and my work. Kelvin Droegemier, Lans Rothfusz, Gina Eosco, and Kim Klockow for inviting me to be part of vital conversations about our shared commitment to the Weather Enterprise. Laura Furgione, who first helped me enter the forecasting world. Russ Schumacher and the SPREAD collective. I have enjoyed our friendship and collaborations. Randy Wynne and my IGEP peeps, thank you for your support. AMS Policy Colloquium family, you have made this social scientist feel welcome. My chosen family: Becca, Julie, Greg, Chris & Kathe, James & Sue, David & Janet—I think of you all the time. Thank you for choosing me back.
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My siblings, especially my sister, Angela, and her husband, Devin; my brother, Micah and his wife Tina; and my brother, Eddie. You are the core of my world. And to the satellites that circle us in their curiosity and love, my nieces and nephew: Topaz, Chloe, and Lucas. To all the Websters, especially Sally, my second mom; Don and Shirl, my adopted parents, and their sweet babies; and the Wolfes, Azure and Jeff. I’m so lucky to have you all in my life. Thanks to my dad, who helped bring me into the world. And to my mom for making it mean something. To the crazy kitty kids I have shared with my partner, Dane: Lily, Bumble, Sissy, Peanut, and Roo. Thank you for keeping me sane. And to Dane, my world. Thank you. Always. There are no words. This dissertation was funded, in part, by the Interdisciplinary Graduate Education Program in Remote Sensing at Virginia Tech; an Advanced Study Program Visiting Graduate Student Fellowship from the National Center for Atmospheric Research; and a VORTEX Southeast research grant from the National Oceanic and Atmospheric Administration (NOAA Award NA15OAR4590233).
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Table of Contents Dedication ................................................................................................................................ iv
Acknowledgements ................................................................................................................. v
Table of Contents .................................................................................................................. vii List of Figures ........................................................................................................................ viii
List of Tables ............................................................................................................................ ix Preface ......................................................................................................................................... x
Introduction ........................................................................................................................... xvi
Article 1: The Ethic of Accuracy: Troubles in The Man-‐Machine Mix .................... 1 Article 2: Matters of Concern ............................................................................................ 59
Article 3: Weather Ready Nation or Ready Weather Agency? Developing an Ethic of Resilience in the National Weather Service ................................................. 96
Article 4: Compromise and Action: Tactics for Doing Ethical Research in Disaster Zones ..................................................................................................................... 136 Conclusion ............................................................................................................................ 165
Bibliography ........................................................................................................................ 172
Appendix ............................................................................................................................... 190
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List of Figures Figure 1 Meteorological cancer of the man-machine mix according to Snellman ............ 30 Figure 2 Two forecaster roles as envisioned by Snellman. Top: Meteorological cancer of
the man-machine mix. Bottom: Rebalanced man-machine mix with AFOS. ........... 38
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List of Tables Table 1 Types of Accuracy in Weather Warnings ............................................................ 52
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Preface
This dissertation has brought me full circle in many ways.
In 2008, I joined a group of Virginia Tech undergraduates who were completing
their field studies course in meteorology, known informally as the “storm chase
experience.” I had just moved from Kansas where I completed my studies in creative
writing and where had fallen in love both with the green lines of tall grass characteristic of
the Plains and storms that blossomed along the horizons throughout the spring and
summer. Tornados, in particular, captured my attention. Like many meteorologists I would
later interview, I found the spinning winds awe-‐inspiring and terrible, what the Romantic
writers in nineteenth century England called the Sublime. Joining the storm chase group as
a nonfiction writer, I hoped to better understand what motivated novice meteorologists to
take such risks with their lives. I couldn’t know then that what I experienced during this
trip would also transform my career.
As a chase group, the nine undergraduate students, two storm chase leaders, and I
spent sixteen days threading storms and waiting out their absence as we made our way
West across the continent. During one of what chasers call “down days,” or days when
there were no storms to chase, we visited a small town of Saragosa, Texas. On the evening
of May 22, 1984, the community had endured an F-‐4 tornado, a designation of the Fujita
Scale that signified winds at somewhere between 207-‐260 miles per hour—what Fujita, the
scientist who created the scale, called “devastating damage.”1 Over thirty years later, the
town seemed to have recovered in small ways, with patchwork repairs to some buildings, 1 Fujita, “A Proposed Characterization of Tornadoes and Hurricanes by Area and Intensity.”
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though just as many had been abandoned. A few foundations stood empty, reminding any
visitor of the power of such storms and the complexities of recovery. Our chase leader
would tell us that the main notice the Spanish-‐speaking community had of the impending
storm came from a spotter who ran into a building where a Head Start graduation
ceremony was underway and yelled, “Tornado!” They never received the official warning
issued 126 miles away at a forecasting office in Midland. People raced to put their children
under tables and chairs stacked against one of the walls of the cinderblock building. A
woman who survived that day recounted to us how she lost her mother, an aunt, and a
neighbor. She told us that the people in her town had no idea the tornado was coming, and
that she hoped as meteorology students that our group would do more to ensure that a
tragedy like the one she lived through didn’t happen again.
Several questions came to mind as our small group later studied the stone memorial
just outside the new community center, which was dedicated to the thirty individuals who
lost their lives that day: Why didn’t they get a warning? What was meant by “warning” or
“the warning system”? Why had so many died? And who or what was responsible for what
seemed like a grave miscommunication? What I didn’t know then is that these questions
would lead me back to graduate school and into the field to learn about the warning system
and different participants attending to it. It would also lead me to become an active and
critical participant in the “Weather Enterprise,” or a network of agencies, organizations,
and industries from three broad sectors: meteorologists from the private sector, such as
broadcast meteorologists and those in industry; meteorologists from the public sector,
such as the National Weather Service (NWS), who work in the federal government; and
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those in the academic sector, including universities and professional societies, who
contribute scientific research and investigate operational issues.2
A few months after the chase ended, I would interview the Meteorologist in Charge
of forecasting operations for the NWS office in Midland, Texas. He had issued the tornado
alert for Saragosa at 7:54 pm on May 22, and, the next day, had walked through the
damaged community in order to assess the storm’s severity.3 During our conversation, he
placed photos of the destruction on the table in front of me, describing as he did the
fragmented buildings, the search for bodies in the wreckage, and the frantic attempts by
local meteorologists to understand how, in spite of their accurate warnings, this town had
not known about the tornado until seconds before it hit. An official assessment report
conducted by the NWS concluded of this event, “If the ultimate criterion in judging the
effectiveness of Saragosa’s warning system is whether the tornado warning reached those
at risk, it must be concluded that the warning system failed.”4
Retired after forty years as a meteorologist, the man I interviewed that day pointed
out the timeliness of the tornado warning, noting especially the unusually long “lead time”
given for people in Saragosa to take cover. “They had nearly thirty minutes to get to a
shelter before the tornado hit the town. Even today, that’s unprecedented.” A few years
later, in 1989, he was awarded the American Meteorological Society’s National Exceptional
Specific Prediction Award for his role in warnings issued across Texas that afternoon.
However, it was clear to me that he found this accomplishment difficult to marry with the
images of destruction he’d witnessed in that town, and perhaps with his own sense of 2 American Meteorological Society, “State of the Weather and Climate Enterprise.” 3 Layman, Personal Interview. 4 National Research Council, “Saragosa, Texas, Tornado May 22, 1987: An Evaluation of the Warning System,” 27.
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responsibility. What haunted him, he said, was the knowledge that the people of Saragosa
didn’t get his warning. “They should have,” he said. The number of people who died “on his
watch”—a refrain common in my conversations with NWS forecasters—troubled him still.
“We need to make sure this doesn’t happen again.”
What struck me then, and still does today, is the great effort forecasters commit to
getting predictions correct and how this determination lives alongside the concern they
express for their various publics when severe weather threatens. In part, forecasters’
interest in their various publics comes from a situatedness of place. Forecasters reside in
the communities they serve, attending community events, school functions, or church with
neighbors who frequently ask them about their work, the latest forecast, an upcoming
storm. Their children go to schools and their spouses work in businesses that are
potentially in harm’s way in bad weather. Their concern becomes most salient when
people’s lives are at stake, which can occur during the more immediate threats of
tornadoes or and heavy rain or the gradual dangers of hurricanes or snow. This immersion
in a risk society5 of environmental hazards that forecasters help adjudicate and generate
means that, like Beck’s experts, they cannot escape the responsibility for, nor the effects of,
their expertise. Understandably, this troubles them.
Protection of their respective “public” sits at the heart of forecasters’ work,
motivating them as scientists and as public servants. Yet, notions of how to protect and
what protection means are changing and are doing so in ways that have consequences for
their agency and for people affected by weather on the ground. On the one hand, many of
the activities, practices, and trainings that forecasters engage in arise within the context of
5 Beck, Risk Society: Towards a New Modernity.
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their profession’s commitment to predictive precision. Daily forecast technologies, warning
procedures, damage assessment protocols, communication strategies—these reflect norms
of accuracy and efficiency. Interestingly, official agency discourses convey such values as
mechanisms for safeguarding their publics.6 Accuracy is a means of protection, a way of
preventing harm. Accuracy imbricates public safety. But as the forecaster I interviewed that
day lamented, a technoscientific fix is usually not enough. As is suggested in more recent
NWS agency documents and through my observations of forecasters in action, protection of
life may be more successful if accuracy is co-‐constituted with attention to relationships
built with people across a spectrum of public safety experts and with different lay publics.
The ethical dimensions of forecaster knowledge and expertise manifest in various ways,
predominantly through a commitment to accuracy, efficiency and the like, but also, I
suggest, to care.
Over the last few decades, social scientists from disaster and hazards disciplines
have increasingly been invited to join the Weather Enterprise in the common goal of
improving the warning system to minimize loss of life from dangerous weather. Many in
this community characterize partnerships between physical and social scientists such that
the former offer only insight into the atmosphere while the latter offer complementary
insight into society. I see my role as one who complicates these framings, showing how
conceptions of the one—forecasters, the atmosphere, and “the public”—are intertwined
with the other, as well as with the technologies and knowledge production of prediction.
For example, hazardous weather means little if there are no people to experience it.
Similarly, forecasters determine hazards based on both atmospheric criteria instantiated in
6 Goodsell, “U.S. National Weather Service.”
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their technologies and their “impacts,” or effects, on humans. And the atmosphere is
conceptualized through predictive apparatuses as weather only in the context of the people
who name and live it. Such mutual constitutions are likewise evident in how forecasters
and publics talk about one other, the expectations they have of one another, and their
sociotechnical imaginaries of what they and the other ought to be.7
This dissertation attempts to answer some of the questions I had that day in
Saragosa—and many more I’ve wondered about since. For now, I focus on forecasters and
examine key norms and values that have played an important part in how they have seen
themselves in relationship to society over time and how they are framing this connection
for the future. I hope to offer alternative images of what their role might look like and how
examples of such choices already exist in their daily work. I do so through an examination
of various ethical dimensions of forecasters’ practices, including the historical and ongoing
debates about their proper role in society and dominant images of who they might be in
relationship to those people they serve. In this sense, my work is normative and as such,
prescriptive. Such an analysis, I hope, finds meaning among my colleagues in the Weather
Enterprise and Science and Technology Studies disaster scholars, and suggests possible
changes in operational meteorology that may lead toward more positive and just futures.
7 Jasanoff, “Future Imperfect: Science, Technology, and the Imaginations of Modernity.”
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Introduction “Oh, would some power the gift give us/ To see ourselves as others see us!?” -‐-‐Robert Burns, “To a Louse, On Seeing One on a Lady's Bonnet at Church” 1786
Weather prediction is an ethically complex profession in which scientific experts
create forecasts and issue alerts that have direct effects on public safety. Forecasters
employed either publicly in government agencies or privately in various industries share
the common goal of accurately and quickly predicting weather phenomena that threaten
lives. In this sense, their work is high risk, as it can result in material consequences for
members of their publics, including the possibility of death or injury. This effort is
conceptualized as one based in responsibility, a professional and personal ethic that
emerges from their relationship with those they call “customers” or “users.”8 A guiding
principle of weather prediction, then, is accountability for forecasts of future atmospheric
conditions and an obligation to issue them in a timely manner such that individuals can
make decisions to keep themselves and their loved ones safe.
In the discourse of weather prediction, tension emerges between forecasters’
dedication to predictive accuracy (e.g. their “passion” for their science) and concern over
public welfare (e.g. their “service” to their communities) as they consider their own role in
society. In their daily forecasting work, they spend their days characterizing the
atmosphere by assimilating output from computer models and observational
instrumentation with their knowledge of meteorology, personal experience in an
operational setting, and familiarity with local weather. That is, they see their role in society
8 “Weather Ready Nation: NOAA’s National Weather Service Strategic Plan.”
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as scientific practitioners skilled in the science of forecasting. When dangerous weather
threatens, they draw on this expertise under circumstances of high uncertainty to make
decisions about which populations sit in the path of potential severe weather. Here they
see their role as multiple. They function, as what I call “managers of risk,” or those who
adjudicate immediate threats of dangerous weather; “watchful guardians”9 of their publics,
which reflects the agency mission to “protect lives and property;”10 and epistemic
authorities within a local network of public safety officials in their role as scientific experts.
Such tension is especially visible among those employed in the National Weather Service,
where weather alerts, called “warnings,” originate.11
National Weather Service warnings and their attending ethical dimensions are
unique in the context of prediction and deserve special attention. Issued for weather
phenomena classified as “severe” or “extreme,” forecasters adjudicate which types of
threats meet criteria for severe, both collectively within the forecast office and individually
at their workstations. Decisions over whether and when to warn their publics may be
partially made through consultation with other forecasters and public safety officials;
however, attribution of “successful” predictions and blame for “bad” ones falls squarely on
the forecasters and their agency. For example, each year stories circulate in the media
about how people who survive severe weather didn’t know it was coming. Headlines
announcing storms came “without warning” suggest that forecasters neglected to do their
jobs, or did so inadequately. Interviews with individuals who claim a lack of notice suggest
9 Goodsell, “U.S. National Weather Service,” 79. 10 National Oceanic and Atmospheric Administration, “NOAA’s National Weather Service.” 11 National Weather Service Act of 1978.
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to scholars that warnings only count if they come through mechanisms and with advice
that people can access and act on.12
Such epistemological claims reveal the ways that warnings function as boundary
objects between different groups and the multiplicity of valences for what gets classified as
a warning.13 In recent years, forecasters have been asked to expand their knowledge and
responsibility in warning operations beyond the creation and dissemination of their
warning as a “product.” They are called upon by their own agency to be increasingly
responsible for developing “deep relationships” with partners to understand their needs
and learning about methods for successful communication of information to different kinds
of publics.14 That is, forecasters must understand the variety of ways warnings are
conceptualized, defined, understood, and counted. And they must contextualize and situate
these meanings within the sphere of individual users’ needs and decision thresholds. This
work is not necessarily new for forecasters who have, for decades, worked with partners to
improve their advice. The agency’s framing of this effort, however, is new in what it
suggests about the future forecaster. As NWS forecasters move towards what they have
begun to call impact-‐based decision support services, or “interpretive,” activities that
extend and challenge common classifications of warnings, they have also begun to question
this new role of what forecasters ought to be.
12 Morss, Demuth, and Lazo, “Communicating Uncertainty in Weather Forecasts: A Survey of the U.S. Public.”; Morss et al., “Improving Societal Outcomes of Extreme Weather in a Changing Climate: An Integrated Perspective”; Lazo, Morss, and Demuth, “300 Billion Served: Sources, Perceptions, Uses, and Values of Weather Forecasts”; Schumacher, “Multidisciplinary Analysis of an Unusual Tornado: Meteorology, Climatology, and the Communication and Interpretation of Warnings,” 2010. 13 Star and Griesemer, “Institutional Ecology, ‘Translations’ and Boundary Objects: Amateurs and Professionals in Berkeley’s Museum of Vertebrate Zoology, 1907-‐39.” 14 Swanson-‐Kagan et al., “Update on the NWS Operations and Workforce Analysis.”
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In the high stakes scenarios of warnings, where risk is immediate and outcomes
uncertain, frictions arise as forecasters make decisions about what information to share,
how to narrate and display predictions, what types of alerts to issue, and why. Conflicts
likewise materialize based around differences in individual and institutional norms,
forecaster experience and skill, and practices specific to forecasters’ respective local office
culture. Attending to these tensions are beliefs about the purpose of forecasters, about “the
public,” and about the future of their enterprise. Together, these constitute the
sociotechnical assemblages of predictive work, which perform and create complex ethical
dimensions for forecasters. .
This dissertation examines the weather warning process in the National Weather
Service through important, though often unexamined, ethical dimensions that arise in
forecaster work and agency goals. I argue that the agency emphasizes accuracy and
timeliness at the expense of other values, which leaves forecasters unclear about several
aspects of their professional identities. This is not to say that accuracy is not important. It
is. Success could not be had without it. Yet viewing their science through a dominant ethic
of accuracy calls into question their role in society in its exclusive focus on them as
predictors of daily weather, a skill that is increasingly troubled as computer models
improve. Competition with their machines affects those strategies forecasters use to
maintain their scientific authority as their work moves away from daily prediction and
toward an emphasis on relationships. And it shapes their efforts to adjudicate institutional
norms that support an ethic of accuracy with those of care and concern for their
communities. An examination of these tensions is important to identifying those non-‐
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dominant ethics that exist in their work and to providing alternative images of what it
means to be a weather forecaster in service to society.
I propose that the National Weather Service and its meteorologists should rethink
forecasting science through their relationship to multiple publics, making visible in their
trainings, practices, and identities both the precision and care that already attend to their
work. I offer the concept of “empathetic accuracy” as an alternative ethic that allows
forecasters to focus on predictive precision through their commitment to a relational ethic
with their publics. While forecasters might read this as a suggestion to include concerns
outside the scope of their expertise and knowledge as scientists, I would suggest they
already exist as a crucial part of both. By making empathetic accuracy visible in their
identities and thus their profession, forecasters and administrators can better meet their
goals to create an agency—and a personal predictive practice—more responsive to
people’s situated lives. In this sense, I ask questions about what counts as forecasting
knowledge and who decides.15
I selected an emphasis on ethics for four reasons. First, the NWS mission is
expressed as an ethical commitment to protecting their publics from harm. This creates an
opportunity for analysts to explore what such an obligation affords in terms of the agency’s
technoscientific developments and practices, what responsibilities it creates between
forecasters and relevant individuals or groups, and how it gets expressed in agency policies
and procedures. An analytically rich and little understood arena, investigating the ethical
dimensions of weather warnings offers me, the analyst, the chance to use “ethic” as a
language of visibility, revealing norms and values that are difficult to discern within the 15 Haraway, “Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective.”
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sciences of operational meteorology and their attending sociotechnical and political
apparatuses.
Second, I share their commitment to protecting lives. I have participated in this
community for nearly five years as a social scientist, a designation forecasters give to those
who study the human dimensions of weather extremes. While the term social scientist may
be an imperfect universal label for all who work in social, cultural, and human concerns of
weather disasters—there is no comparable label for those doing humanistic work, for
example—it is the label forecasters are familiar with and the one they have given me. Thus
it is the one I choose to perform.
In this role, forecasters and agency officials have turned to us with requests for help
in revealing and addressing problems in the warning process, though often they have in
mind an agenda aimed toward understanding “the public” and not necessarily themselves.
My critical participation16 has created for me an ethical obligation to respond. Thus, I have
spent the last five years “studying up”17 within their institution to identify places of
possible intervention—and one place is ethics. An emphasis on ethics allows me to push
forecasters’ concerns into places where I think they ought to be, in this case, toward an
examination of the values operating in the domain of “the expert” forecaster, which is what
I explore in articles one and two. Encouraging a symmetrical analysis of the warning
process from both expert and public points of view becomes part of my fulfillment of the
analyst’s ethical obligation.
16 Downey, “What Is Engineering Studies For? Dominant Practices and Scalable Scholarship,” 2009. 17 Nadar, “Up the Anthropologist: Perspectives Gained from Studying up”; Priyadharshini, “Coming Unstuck: Thinking Otherwise about ‘Studying Up.’”
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Third, as an STS scholar, an emphasis on ethics is one important and common
disciplinarily way for me to participate. Understanding what forecasters value in their
knowledge production and practices reveals how they produce a certain vision of society. If
it is one that is “resilient” against severe weather and the multiplicity of disasters that
might delay such ability, then someone needs to help unpack what inflections resilience
might carry and for whom and at what cost, as I do in article three. Related to this is how
these values in society connect to those in their agency, through what mechanisms,
discourses, and activities? Ought these be the values forecasters should expect of their
publics? Of their own profession? How ought lay people factor into decisions about
weather warnings and technological development? How do they already? In short, I want
to be well prepared to “fill the ethics seat” in weather disaster contexts, as many in STS
have suggested we might be called to do.18 My expertise, then, lies in offering an
ethnographic, historical, and discursive exploration of ethics that have shaped and been
shaped by NWS sociotechnical developments, as well as those concerns frequently raised
by forecasters in forecast offices, professional society meetings, and research agenda.
Finally, this dissertation reflects my critical participation as a Disaster STS scholar.
Working out my understanding of important norms and values for forecasters, I believe,
underscores the embodied nature of my own work, which I address in article four. It
highlights what Cohen and Galusky suggest is “the lived cultural and personal experiences
of scholars [like myself] and [our] scholarship within larger sociotechnical systems.”19 As a
18 Fisher, “Ethnographic Invention: Probing the Capacity of Laboratory Decisions”; Fisher, “Public Science and Technology Scholars: Engaging Whom?”; Woolgar, Coopmans, and Neyland, “Does STS Mean Business?”; Schuurbiers, “What Happens If the Lab Does Not Stay in the Lab?: Applying Midstream Modulation to Enhance Reflection in the Laboratory.” 19 Cohen and Galusky, “Guest Editorial,” 3.
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friend and colleague to many forecasters, I employ a relational ethic with those I study, one
that, as Carolyn Ellis has written, “recognizes and values mutual respect, dignity, and
connectedness between researcher and researched, and between researchers and the
communities in which they live and work.”20 It is a difficult ethic to navigate at times as I
listen, learn, and critique. But it is worth the effort.
Site Relevance & Methods
The National Weather Service is the government agency mandated by Congress
since 1870 to collect and assess information about the atmosphere and from this “collage”
of “highly complex information”21 to construct and communicate daily forecasts. Likewise,
forecasters employed in one of 122 weather forecast offices (WFOs) across the country
have been given sole responsibility for issuing warnings to the general public during severe
weather. In part, the NWS mission, as stated in strategic plans, is to “protect lives and
property,”22 a goal that forecasters embody in multiple ways throughout their operational
work. Warnings inflect this mission as an important part of the larger sociotechnical
infrastructure that arranges their daily labor, their practices, and their relationships.
Imbricated within this scientific enterprise are the political, ethical, and social dimensions
of prediction, which likewise affect how forecasters see themselves relative to their
profession and their publics. The National Weather Service and its forecasters sit at the
20 Ellis, “Telling Secrets, Revealing Lives: Relational Ethics in Research with Intimate Others,” 4. 21 Daipha, “Weathering Risk: Uncertainty, Weather Forecasting, and Expertise.” 22 “Weather Ready Nation: NOAA’s National Weather Service Strategic Plan.”
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center of predictive controversies in their role as the originators of warnings, or the
“wholesaler of weather information,” as Daipha puts it.23
Ethnographic Work
I began my observations of the National Weather Service forecasting and warning
process within their workspace, or Weather Forecast Offices (WFO), in 2012. From October
2012 to July 2013, I watched forecasters in an office in the mid-‐Atlantic work once a week
for four hours, learning the jargon, technologies, and practices of forecasting. I kept 427
pages of field notes and audio recorded 50 hours of conversations and daily work with
meteorologists. From 2014 to 2016, I spent nine months in two other forecast offices, one
in the West (8 months) and the other in the Southeast (1 month). In the former, I sat with
forecasters from January 2014 to August of 2016. For the first three months, I arrived at
the office for six-‐hour visits twice a week to acclimate to the particulars of this office’s
approach to forecasting and warnings. Beginning in March, I followed the weather, arriving
on days leading up to anticipated tornadoes or flash flooding, stayed throughout the
warning process, and joined forecasters for any damage surveys or post-‐event debriefings;
I interviewed 22 meteorologists, including those who work on their technologies. In the
latter, I spent 2 weeks specifically watching forecasters discuss and issue warnings on
overlapping threats of tornado and flash flood, and discussing the technologies and
strategies used to create them. I interviewed 11 meteorologists over the course of another
week. All told, I spent 93 days in these two offices, audio recording all interactions (529
hours).
Further I have been critically participating as a presenter and organizer of annual
23 Daipha, Masters of Uncertainty: Weather Forecasters and the Quest for Ground Truth, 28.
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conference sessions attended by NWS meteorologists and many others in the Weather
Enterprise. My participation in these meetings not only helps me better understand
forecasters but how they fit into the larger weather community and interact with different
groups. For the past two years, I have served as the chair of the Societal Impacts
Committee for the National Weather Association and have presented at and won awards
for the presentation of my work. I am currently on the American Meteorological Society’s
Board of Societal Impacts and a member of a special conference planning committee for a
symposium to be held in 2017 called “Special Symposium on Individual, Social, and Cultural
Observations in Weather and Climate Contexts.”
As part of the social sciences community for these two conferences, I’ve helped
shape agenda and speaker choices, specifically becoming known in these circles as
someone who can speak to ethical dimensions of warnings and as someone who researches
NWS forecaster challenges and technologies. More importantly, becoming an integrated
member of the community keeps me abreast of issues important to forecasters and those
that I believe ought to be. For example, I’ve given two conference presentations on the
value of building relationships through the Integrated Warning Team initiative, a
grassroots assemblage of people from the weather community in a local area. I’ve argued
that there are other key groups missing from these meetings, including those not
traditionally seen as partners in the Weather Enterprise: storm chasers, storm spotters,
military facilities, administrators of nursing and retirement homes, representatives from
the general public, and those who direct faith based and community organizations.
Finally, last year I was invited to join in an NWS initiative called Impact-‐Based
Decision Support Services (IDSS), by helping facilitate and organize a yearlong series of
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webinars. To date, there have been eight webinars. Agency administrators are developing
ideas for what will count as IDSS in light of an agency restructuring of NWS staff and
reexamining the function of the agency. Amid Congressional and private sector criticisms
about the budget and the need for the organization to maintain a structure that has been in
place since the 1980s, the agency hired a consulting firm, McKinsey & Company, to help
administrators develop a new plan that “evolves the culture” of the NWS. As I discuss in
article three, IDSS is one of the important initiatives gaining support in the new philosophy
of the agency, which focuses on “deep relationship” with core partners.24 Participating in
this series of webinars has given me insight into where the operational forecasters see
themselves and how they’re interpreting the initiative, and creating IDSS activities in light
of their daily work.
Archival and Historical Work
I have drawn from online historical archives of scholarly journals, monthly
newsletters, and conference proceedings in the meteorological community. I also spent
three weeks at the NOAA library at the National Center for Atmospheric Research in
Boulder where I photocopied 235 pages of memoranda, technical manuals, and conference
preprints about NWS warning technologies. Further, I’ve collected materials from private
archives in individual offices at the Global Systems Division in the NOAA Earth Systems
Research Lab, also in Boulder, including two training manuals about warning software.
Aside from interviews and observations, this material is the best source for understanding
how forecasters have identified challenges related to their profession and their role in
society, as well as the assemblages of sociotechnical, ethical, and political discourse of
24 Swanson-‐Kagan et al., “Update on the NWS Operations and Workforce Analysis.”
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forecasting from the 1970s to today. From these visits, I have collected 3,372 pages of hard
copy reproductions and another 347 digital pages of material related to forecasting,
weather prediction, and sociotechnical developments.
While not all of the material I’ve collected from archives or obtained from
ethnographic work over the past four years is explicitly represented in this dissertation—
scholars tend to collect much more than we can use in any one project—it informs my
work in tacit ways. Continual exposure to this community, and my participation within it,
complements this material such that I feel I know NWS forecasters as an interactional
expert.25 As evidence of this role, I’m frequently called on to represent their point of view in
social science circles within the Weather Enterprise, and though there are many problems
with representation, forecasters themselves have nominated me to speak on their behalf
and have invited me into their offices. Their generosity and trust humbles me.
Terms and Literatures
Because this dissertation takes the form of a series of manuscripts, detailed
literature reviews are included in each article and tailored to their specific purposes. Other
literatures relevant to the form or purpose of the articles can be found in the commentary
prior to each article, when necessary. Still, here I offer a few notes about overarching
connections to STS literatures that shape my assumptions and approaches to my
ethnographic and historical work.
By emphasizing an “ethic of ” particular norms and values emerging within the NWS
warning process, I wish to distinguish my use of the term from that of Ethics, the larger and
systematic philosophical inquiry into virtues, morals, and metaphysics generally.
25 Collins, Evans, and Gorman, “Trading Zones and Interactional Expertise.”
xxviii
Specifically, I am looking at particular values, or ethos, that arise within situated
circumstances of weather warnings and in the discourse of forecasting as a profession—an
applied ethics of weather prediction, so to speak. Each ethic I call out, then, is a localized
and contextual normative commitment or value that co-‐exists with other institutional and
personal norms, sociotechnical infrastructures, and practices and policies within the
National Weather Service.
Other terms merit clarification. Weather prediction in this dissertation subsumes
both forecasting and warning practices, reflecting the fluidity of forecasters’ work in both
arenas of expertise. And I explore such terms through practices and assumptions made by
the National Weather Service, my site for this study. Forecasting, as other scholars have
shown, consumes the bulk of their predictive activities.26 Warnings are a special kind of
activity, one that occurs more infrequently in many offices and requires different software,
skillsets, and experiences to do well.
Weather and weather events are likewise terms that merit closer critical attention, a
task that is outside the scope of this dissertation. I use them in the dissertation as
forecasters might: weather is the interaction of atmospheric processes that create
phenomena, such as clouds, precipitation, or storms. Another common use is as a reference
for a lack of threatening or dangerous weather, as in “No weather” or “fair weather.” Severe
weather, in contrast, is the kind of atmospheric conditions that have the potential to affect
people on the ground in negative ways. Weather events are singular occurrences of
weather or related occurrences that happen in a short period of time. They are bound by
spatial and temporal concerns. An event might be a solitary storm, like a hurricane or 26 Fine, Authors of the Storm: Meteorologists and the Culture of Prediction; Daipha, “From Bricolage to Collage: The Making of Decisions at a Weather Forecast Office.”
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tornado, for example, or a cluster of storms that move together across the country over 24
hours, like a blizzard or “outbreak” of tornadoes.
Finally, what counts as a forecaster is not always easy to determine. The American
Meteorological Society and the National Weather Association offer multiple credentials and
educational criteria27 that must be met to practice forecasting and to do so in a manner
recognized as ethical by their profession.28 For example, an individual on social media
might make a prediction about a particular weather event but to attain the expert label of
forecaster, they have to perform the educational and social aspects of the profession. For
this dissertation, I am focusing on individuals who work in the specific role as forecaster
within the National Weather Service and assume that staff hired to work in this agency
meet these educational or equivalent requirements, though training on the specific
sociotechnical aspects of forecasting practice, as well as their personal skill, can be
localized and idiosyncratic.
Ethics of Warnings Literature
In focusing on the ethical dimensions of weather prediction, my work is in
conversation with several scholars in Science and Technology Studies. First, I find common
ground with those sociologists of science who have explored norms common to science and
technology itself and to specific instances of knowledge production. For example, while I
am not debating the norms and ideologies of science, as Merton, Mitroff, and Mulkay did, I
27 American Meteorological Society, “The Bachelor’s Degree in Meteorology or Atmospheric Science”; American Meteorological Society, “What Is a Meteorologist? A Professional Guideline.” 28 Hill and Mulvey, “Business Ethics for Professional Meteorology: Expectation and Satisfied Customers”; Hill and Mulvey, “The Ethics of Defining a Professional: Who Is a Meteorologist?”; Meisner, Hill, and Mulvey, “Ethics for Government Meteorologists”; Hill and Mulvey, “Resources and Guidance for Ethics and Personal Conduct in Meteorology.”
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agree with Merton that norms may be “legitimatized in terms of institutional values” and
“are in varying degrees internalized by the scientist” in ways that shape and are shaped by
their practices and activities.29
In the case of the forecasters, values of accuracy as projected through the image of
“the man-‐machine mix,” which I discuss in the first article, may indeed create a “story book
image of [forecasting] science,” an ideal, that deviates from their actions.30 And like Mitroff,
I see not only dominant values operating in forecasting, like accuracy, but, at the same time,
their counter.31 In the second article, for example, I aim to juxtapose the more objective
nature of accuracy explored in article one with a very intimate accounting of an ethic of
care in action. I suggest these two are less contrasting values—accuracy and care—as
complementary, and in the context of protecting lives, essential to one another. Finally, I
see the science of forecasting as moving toward an imperative implicated in their own
sense of responsibility for their communities’ safety. In article three, I suggest this is seen
most clearly through an ethic of resilience, one that is as much about their own survival as
a profession as about those they protect. As Shapin and Shafer point out, "Knowledge, as
much as the state, is the product of human actions.”32 We are also, then, responsible for
what we know.
These scholars are part of a tradition paralleled by many in the philosophy of
science and technology that calls attention to the social contexts and problems of scientific
29 Merton, “The Normative Structure of Science,” 269. 30 Mulkay, “Norms and Ideology in Science,” 647. 31 Mitroff, “Norms and Counter-‐Norms in a Select Group of the Apollo Moon Scientists: A Case Study of the Ambivalence of Scientists.” 32 Shapin and Schaffer, Leviathan and the Air-‐Pump: Hobbes, Boyle and the Experimental Life, 344.
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and technological development.33 For example, I explore the ethical dimensions of
meteorological science that get folded into practice and outcomes in order to begin a
conversation about how and whether we ought to improve this science. That is, similar to
scholars like Longino, I believe we ought to include in our analysis attention to the “roles
and values, interests, and relationships in the social and cultural context of science that
play in scientific judgment” and the impact of “science and science-‐based technologies” on
society.34 I look explicitly at the shift in values and interests in the National Weather
Service and argue that attention to the ethic of resilience through care and accuracy opens
up an opportunity to better understand, and perhaps improve, forecasters relationship
with their communities.
Focusing on the warning process aligns me with those, such as Winner and Jonas,
who ask questions about the politics and power of technology. Within the ethical frame of
“objects” and “dynamics” of technology, one might ask, “What are the chances and what are
the means of gaining control of the process [of technological invention] so that the results
of any ethical … insights can be translated into effective action?”35 I am less concerned
about questions over control of technology processes in meteorology, per se, than about
posing questions regarding expert assumptions of their role in society before, during, and
after technological developments occur. Thus, my normative undertaking suggests action
and thus mirrors advice that some philosophers give with regard to the technoscientific
enterprises in which they choose to engage. Additionally, like Fuller,36 I ask about how we
in STS are to make decisions about the participation of different groups in the directions 33 Ravetz, Scientific Knowledge and Its Social Problems. 34 Longino, “How Values Can Be Good for Science,” 127. 35 Jonas, “Toward a Philosophy of Technology,” 41. 36 Fuller, “The Future of Science and Technology Studies.”
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and governance of society—that is, for any knowledge problem, how do we move (and
when and why) from is to ought?37 Not only is this question present in the first three
articles which explore the content of is and ought in the processes of knowledge production
in the forecasting community but in my role as a disaster scholar. Article four takes up this
question most directly, suggesting that there is no easy answer given the state of
compromise that attends to such questions. However, an ethic of relationality offers the
STS scholar one potential guide in moving forward with suggestions.
I make no claims to being a philosopher or to having been trained as an ethicist in a
formal way. Instead, I’m philosophically inclined toward normativity because forecasters
have asked for my help and I feel that acceptance into their community compels me not
only to address concerns they have but also to note when I see issues that ought to concern
them. I have spent significant time with them, sitting with them for nearly two years, and as
such have a unique understanding of their work that allows me to make such judgments. I
do so with caution and with the knowledge that I am only a small part of a much bigger
assemblage of policies, practices, and interests that shape change. Still, I take my work
seriously, just as forecasters—and my colleagues in the Weather Enterprise—do, too.
I wish to note here that like others who expose the complex entanglements to which
their line of inquiry points, I respect the expertise and dedication of those that I participate
with.38 Forecasters take seriously their science and their hard won understanding of local
weather phenomena, just as they do their commitment to helping people stay safe. They
know the atmosphere well and are skilled at their craft. Yet the intersectionality of many 37 Turner, Explaining the Normative. 38 Lochlann, Malignant: How Cancer Becomes Us; Gusterson, Nuclear Rites: A Weapons Laboratory at the End of the Cold War.; Traweek, Beamtimes and Lifetimes: The World of High Energy Physics.
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social, political, ethical, legal, and public aspects of their work and its affect on their publics
often fall outside this expertise, or their ability to act on them—a point many forecasters
would readily acknowledge. One might think of these as subsumed under the label of non-‐
meteorological issues, those that are imbricated with their knowledge and technologies, of
course, but in many ways get masked by the daily rhythms of weather prediction.
As predictive experts engaged in the communication of weather threats, literatures
that theorize the cultural, social, and constructed nature of risk also find relevance here.
The projects of risk assessments and risk communication, which attempt to render harm
calculable, play an important role in the classification and interrogation of disasters, as well
as their prevention.39 Acknowledging the multiple valences of risk—as negative in its
identification of dangers, as positive in its explanation of dangerous activities—situates
forecasters as risk managers amid debates over trust, authority, and confidence.40 This
expertise is both essential to their legitimation as scientific practitioners but also becomes
a potential site of controversy in their current relationship with “the public.” Outdated
assumptions of what constitutes communication and expectations of public awareness and
preparedness potentially leave the NWS agency as one that values accuracy as a technique
of governmentality41 and discipline over accuracy as an expression of care and concern.42
Finally, my approach is motivated by feminist epistemologies that seek to
understand the partial, multiple, and situated nature of knowledge, particularly the work of
39 Daipha, “Weathering Risk: Uncertainty, Weather Forecasting, and Expertise”; Dean, “Risk, Calculable and Incalculable”; Douglas and Wildavsky, Risk and Culture; Otway and Wynne, “Risk Communication: Paradigm and Paradox.” 40 Szerszyniski, “Risk and Trust: The Performative Dimension”; Lyng, “Edgework: A Social Psychological Analysis of Voluntary Risk Taking.” 41 Foucault, “Governmentality.” 42 O’Malley, “Risk and Responsibility”; Foucault, “Governmentality.”
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Sandra Harding and her strong objectivity.43 In the context of studying up, forecasters seem
to be located outside the possibility of “a view from below.”44 They are not marginalized,
exempted from power, oppressed in the larger cultural norms that classify such
characteristics by their opposition to dominant structures. Yet within the confines of a
bureaucratic institution, one that reduces their lives to numerical calculations of success or
failure, to institutional policies of risk assessment and communication that make individual
humans invisible to the larger world, forecasters have much to offer. As bodies subject to
the vagaries of shift work that can dehumanize their efforts, forecasters share with the
populations they serve certain vulnerabilities: I have seen women issue warnings during
Braxton Hicks contractions, for example; I have heard stories of men having seizures
because of the difficult hours. As bodies subject to trauma, I have listened to forecasters
talk about the frustrations of failure, anxieties about their futures, and guilt over deaths
they’ve witnessed. Adding their stories to others common in public accountings of disaster
fleshes out more fully the images of objectivity strongly cast by justice.
Manuscript Overview and Justification
As a scholar who plans to seek employment in disciplines (e.g. geography) and
institutes (e.g. NCAR) that value article publications over single-‐authored monographs, I
have selected to complete the manuscript format of the dissertation. This dissertation
follows the requirements of the manuscript format of the Virginia Tech Graduate School
and the Department of Science, Technology, and Society. Accordingly, it includes a
43 Harding, “Rethinking Standpoint Epistemology: What Is ‘Strong Objectivity’?”; Harding, Objectivity and Diversity: Another Logic of Science. 44 Harding, “Standpoint Theories: Productively Controversial.”
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minimum of three (I include four) “high caliber, professional quality papers with
surrounding text providing context, theoretical grounding, and coherence” and it offers a
“well formed body of work” oriented toward “peer reviewed journals.”45’ The connective
tissue that adheres each article in this dissertation can be found in brief commentaries, or
prologues, written before each article, which offer the reader a meta view of the article’s
purpose, audiences, targeted publication venue, and theoretical and thematic linkages to
adjacent articles.
Conceptually, these articles are interconnected through an analysis of key ethical
dimensions that arise within the weather warning discourse, including my own critical
participation in both the meteorological and disaster STS communities. National Weather
Service forecasters, agency documents, and warnings literature. They examine these ethics
through different scales—the historical, the personal, the bureaucratic, and the reflexive—
and each is written for a different audience and to different ends. My normative
commitment is to the forecasters, their ways of knowing and their view from below. In
part, I do so by following those in Science and Technology Studies whose work identifies
“dominant images” of forecasters and their profession and “makes visible”46 alternative
possible futures. I have also identified important norms and values embedded and
emergent in their work and hope to intervene in fruitful ways that demonstrate what these
commitments bring on board with their work. Thus, my work cultivates a conversation
with this community and, I hope, pushes STS to reach out to less known communities of
45 STS Policy Committee, “Department of Science and Technology in Society Graduate Program Rules and Procedures.” 46 Downey, The Machine in Me: An Anthropologist Sits Among Computer Engineers, 5, 18–30; Downey, “What Is Engineering Studies For? Dominant Practices and Scalable Scholarship,” 2009.
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practice and engage unconventional genres and styles as a mode of intervention. This
“making and doing”47 necessitates that I occasionally submerge STS jargon, concepts, and
framings to meet forecasters and other non-‐STS readers where they are and to travel with
them through their concerns, hopes, and fears.
Empathetic Accuracy
I chose for the title of my dissertation a phrase that often appears in forecaster
discussions about their predictions: to err on the side of caution.48 Throughout my
fieldwork, this same phrase has been used as a common reminder not to simply follow the
meteorological guidelines for issuing warnings and communicating risks. Instead,
forecasters are free to sacrifice agency metrics of success to ensure that people in harm’s
way might be safe. In effect, they are putting safety above accuracy. For example, during my
first month at a National Weather Service office, I attended a presentation that one of the
management team had put together for the rest of the staff. Together, they examined the
warnings they’d issued that spring in order to examine how well these polygons mapped to
the local storm reports that verify their warnings. A few phenomena couldn’t be confirmed,
which spurred a lengthy exchange about the merits of issuing warnings for areas like
national forests, where people are unlikely to live or be during a storm.
“Shouldn’t we not issue a warning for an unpopulated area so the storm can’t be
verified anyway?” one forecaster asked.
47 Downey and Zuiderent-‐Jerak, “Making and Doing: Engagement and Reflexive Learning in STS.” 48 National Oceanic and Atmospheric Administration, “National Weather Service Policy Directive 1-‐10: Managing the Provision of Environmental Information”; Englund, “Forecaster: We Erred on the Side of Caution”; Breslin, “10 Things the National Weather Service Wants You to Know about Winter Weather Forecasts.”
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The Meteorologist in Charge, a tall, lanky man, stood up. “We need to find a sweet
spot,” he said, between issuing tornado warnings for unpopulated areas that might require
forecasters to spend “hundreds of hours” tracking down evidence to justify the warning.
“But we also have to err on the side of caution,” he said, just in case someone is there under
that storm, unaware of the danger. A kind of hypothetical thinking, it asks forecasters to
imagine their publics and consider the consequences of following rules or procedures or
even the most likely scenario. It asks them to imagine that one person who might be out
there, who might not see the storm or know how bad it could be.
I evoke the phrase “err on the side of caution” to demonstrate the tension between
accuracy and care. “Caution,” as I understand it, is not about precision. Instead, it is about
concern for people potentially in harm’s way. It values people over numbers. And it
highlights the sense of responsibility forecasters feel for people’s safety49 in light of this
concern, an obligation to the people in their communities often at the expense of their
metrics of accuracy. The Meteorologist in Charge, then, balanced for the group their
collective commitment to accuracy and their commitment to people—to caring for both at
once. It is a care through accuracy that takes into account concern for people. “To err on
the side of caution” likewise points to the multiplicity of ethics that emerge alongside and
in response to one another.
Selecting this phrase as my title also points to my intent with the four ethics
explored within the following and how they speak to my larger project of revealing how
accuracy and care already exist in their practices, often together and inseparably. I term 49 Morss et al., “Flash Flood Risks and Warning Decisions: A Mental Models Study of Forecasters, Public Officials, and Media Broadcasters in Boulder, Colorado,” 2015, 2021.
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this interrelated set of ethics “empathetic accuracy” as a way of illustrating the
interconnectedness of accuracy, care, resilience, and relationality more explicitly.50 It calls
attention to the fact that these values are entangled in forecasting science, co-‐constituted in
forecasters’ technological developments and policies, and reflected in the activities that
direct their interactions with different publics. In fact, an etymology of accuracy in early
sixteenth century Latin is accuratia, or to care or give attention, as in “executed with great
care.”51 Perhaps ironically, then, the linguistic root of the term accuracy contains care. But
accuracy as a term in forecasting is so overly burdened by scientized connotations about
precision, truth, and objectivity, that it is difficult to merely point to its roots and have that
suffice as a way of enrolling forecasters to consider care as an important ethic in their
labor.
Article Overview and Interconnectedness
The selection of the ethics highlighted in this dissertation does some of this work. I
develop my case through a series of articles written at different scales to emphasize the
dimensionality of ethics in my own examination of the warning process; it echoes the
various ways I have observed them in the operational setting. I begin with a genealogical
account of “the man-‐machine mix” as an articulation of accuracy that continues to
perpetuate an identity of forecasters that, I suggest, has never been appropriate to them.
Because variations of this term (e.g. man-‐machine, human-‐machine, human element) are
still used today, it continues to frame what forecaster can and ought to be. It also masks
50 I wish to distinguish this term from empathic accuracy, a term coined in psychology by William Ickes and William Tooke in 1988. It refers to how accurately one person can infer the thoughts and feelings of another person based on concepts like “affect sharing” and “mentalizing.” See Ickes & Tooke (1988) and Ickes (2003). 51 “Accuracy.”
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what the second article highlights, which is the ethic of care, a value that is revealed most
clearly in moments of crisis but exists in their daily work, as well. Forecasters are bound by
care in their desire to protect, as this article shows from a more intimate point of view: that
of specific forecasters and their publics during a tornado. By scaling down to see care in
action, this article offers a glimpse of an alternative ethic that might be more visible in their
professional identities, too. The third article takes a future-‐oriented perspective at the
institutional level and is more normative in its orientation about what I believe forecasters
ought to be. The National Weather Service’s newest initiative Impact Based Decision
Support Services (IDSS) offers an opportunity to emphasize empathetic accuracy. Part of
the fundamental changes the agency envisions among their forecasting staff is a philosophy
emphasizing the need to develop “deep relationships” with their partners. Through
multiple valences of resilience—e.g., knowing people well enough to understand what they
are most vulnerable to and thus would recover from—IDSS has the potential to make more
apparent how resilience operates, and could do more, within their practices. It likewise
reveals a mechanism for deploying empathetic accuracy.
I wrote the final article with a colleague in the Disaster STS community to
acknowledge that work in disaster contexts (and disaster is a contested term, of course)52
is one of compromise. This is most clearly demonstrated, for me, through an ethic of
relationality, or a commitment to ongoing responsibility for researcher actions and how
they affect others that we work with. It challenges us every day to balance ethics valued in
university settings, like Institutional Review Boards or imperatives to publish in scholarly 52 Fortun, Advocacy after Bhopal: Environmentalism, Disaster, New Global Orders; Knowles, The Disaster Experts; Petersen, “Producing Space, Tracing Authority: Mapping the 2007 San Diego Wildfire”; Liboiron, “Disaster Data, Data Activism: Grassroots Responses to Representations of Superstorm Sandy,” 2015.
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journals, with an obligation for mutual respect and dignity between myself and the
forecasters I participate with. It compels me every day to scrutinize what I write and how I
critique—with “great care and caution”53—those with whom I participate. Relationality,
then, offers yet another aspect of the ethic of care operating within the larger weather
community, where most are dedicated to protection of life. This is not unlike the position of
compromise forecasters face in their own work. Forecasters stand in between their
commitments to accuracy and care, between science and safety, and between their history
and their future.
Forecasters and I are in this endeavor together, sharing the same stakes, a sense of
enthusiasm, and passion for figuring out how best to serve one another and those in our
publics. They deserve a complex and reflexive accounting of successes and failure and they
expect those of us who are analysts of their work to hold ourselves—and one another—
accountable. To my colleagues and friends in the weather community who have asked on
many occasions for my help in these efforts, I offer the following articles as a place to begin
that conversation.
53 Latour, “Why Has Critique Run Out of Steam? From Matters of Fact to Matters of Concern,” 246.
1
Article 1: The Ethic of Accuracy: Troubles in The Man-‐Machine Mix
Prologue I first heard the term “meteorological cancer” during the second week of
observations of a local forecast office four years ago. We were talking about a new initiative
to consolidate offices across the National Weather Service, condensing them from the
current 122 local offices to a few regional hubs. It’s actually an old idea, one that gets
revisited during threats of funding and questions about why forecasters in public
government service matter. This forecaster, however, talked about how one of the reasons
for such a possible consolidation was the overreliance of younger forecasters on computer
models in their daily work. Instead of developing their own conceptual model, or snapshot
of the weather, they were simply taking computer model predictions of different weather
variables and using them in their forecast. In effect, he said, “Forecasters are substituting
the computer model for their own knowledge,” a problem he called “meteorological
cancer.” As a metaphor of terminal illness, the phrase struck me as an important vehicle
into current feelings about the future of forecasting as a profession. The tenor of the
metaphor suggests that forecasters are contributing to their own demise, as the computer
models, in effect, “eat away” at their expertise and relevance. The vehicle of the metaphor,
cancer, implies this problem that will spread like a disease and eventually kill the
profession. The phrase “automated out of a job,” in fact, often accompanies the metaphor as
a kind of prognosis.
2
I’ve been privy to other ideas that are degrading to forecasters are and do. There are
those terms that highlight the role of forecasters “over the loop,” for example, making them
managers in the forecasting process. Here they exert their expertise to “quality control”
data as it proceeds throughout a mechanized system. In other contexts, they are mere
communicators, “hand holders” for the public as they explain forecasts more clearly so that
decisions can be made. They are caught, in a way, between the machines that facilitate their
job and the people who rely on them, cast neither as fully scientist nor public servant.
Forecasters are cyborgs in this “leaky” distinction between human and machine,
unclear “who makes and who is made” in this social reality.54 Currently, forecasters in the
NWS are somewhere in the loop of man-‐machine, though recent initiatives like Impact
Based Decision Support Services—addressed in article 3 in this dissertation—are moving
the humans more fully outside the loop to manage it. More disturbingly for some
forecasters is the notion that their main job will then be to quality control the models and
communicate their meaning to different decision makers—a job they engage with to some
degree. To do this new job well, however, necessitates that forecasters learn more about
decision makers, their thresholds, and their “needs” and then tailor expert explanations to
these people. In effect, it potentially transforms the meteorologist as scientist into the
communicator—a demotion in skill for those whose passion is prediction.
The term “man-‐machine mix” entered the conversation much later for me as I was
completing my fieldwork in 2016—a moment I recount in the following article. As I looked
for the origins of this phrase, I discovered it co-‐occurred with “meteorological cancer” in
the same set of writings by Leonard Snellman, an influential forecaster working in the 54 Haraway, “A Manifesto for Cyborgs: Science, Technology, and Socialist Feminism in the 1980s,” 193, 221.
3
National Weather Service in the latter half of the 20th Century. He wrote about the “man-‐
machine mix” as early as 1969 in his attempt to explain the hybridity of humans and
computers that had become part of forecaster’s work. By machine, he specifically meant
two things: first, the computer model, a new simulation technology that embodied accuracy
and speed, two key values of predictive science; and second the forecasters’ minicomputer,
or workstation, which he felt balanced the equation of the mix, allowing forecasters to
complement computer models with their expertise. If abused, computer models could
become a malignancy in their practices. Invoking disease, cybernetics, irrelevancy, and
promise, the “man-‐machine mix” and “meteorological cancer” offer rich concepts that help
me explore what forecasters value—a discourse that still resonates in the community
today.
But how to explore these concepts in ways that connect them to knowledge
production in the profession of forecasting? One way is to examine those scientific values
they invoke and that have successfully resonated with forecasters for over 50 years. One
such candidate revealed in the intersection of human and machine is accuracy, a core value
in sciences generally and a seminal one in prediction specifically. I argue that the “man-‐
machine mix” is an articulation of accuracy in the practices and discourses of the
forecasting profession that points to an image of forecasting science. By demonstrating
accuracy as a social product and overly emphasized dimension of the “man-‐machine mix,” I
hope to likewise reveal that accuracy is a dominant ethic in forecasting that continues to be
re-‐inscribed in the discourse of meteorology. On one level, it does so through the frame of
competition, of forecasters rivaling computer models for daily work even as the machines
increasingly outperform them. The timeliness and accuracy of warnings saves lives, which
4
is one reason to invest in them, but it also saves money, in terms of losses that might be—
agricultural, infrastructural, personal, and the like. That is, “man-‐machine mix” is a term of
political economy. On another level, it does so at the expense of other ethics that when
deployed alongside accuracy may better match the profession’s main goal of protecting
lives. In article two, I suggest the value they should emphasize is care.
For a publication venue, I chose the journal Technology and History, a peer-‐reviewed
journal published quarterly by Taylor and Francis. According to the journal’s submission
guidelines, the journal is a “forum for research on technology in history,” and they welcome
historical contributions that explore a “wide frame” of technology “as knowledge, practice,
and material resource” through analytic and critical approaches. They encourage a broad
range of disciplinary contributions that offer dialogue between history of technology and
the humanities. In their submission category, Historiographic, Field, and Thematic Essays
they refine their goal as one that “offers critical reflection on a broad sweep of intellectual
activity and engages common concerns in explanation within history of technology and
other scholarly fields.” Submissions are limited to 40 pages, including references and
figures.
Tracing the genealogy of the concepts “man-‐machine mix” and “meteorological
cancer” in the context of automation is an historical and cultural endeavor. Although I open
the article with a contemporary scene from my fieldwork in a forecast office, I quickly
address the concept in its historical context, illustrating its attending concerns as part of a
larger societal anxiety about automation in the 1960s. Seen in this light, the “man-‐machine
mix” is one specific instance of a larger sociotechnical frame for addressing the prevalence
of computers in the United States and the apocalyptic fears of artificial intelligence and
5
computer domination. While forecasters are not undereducated and unskilled workers
common on assembly lines in factories or mechanized manufacturing of the early 20th
century—the common setting for what was termed “automation anxiety”—in the 1950s
many entered the profession of meteorology from the military without formal education in
sciences and as skilled technicians facing increasingly more complex routines involving
computer models.
For scholars in Science and Technology Studies, I offer this discussion as a case
study of how anxiety over the loss of scientific authority manifested itself in this specific
profession during the latter half of the 20th Century. For current and future weather
forecasters, I offer this article to contextualize their concerns about the future of their
profession and to suggest they needn’t allow these concepts and values to uncritically affect
their role in relationship to society. They can (and should) consider other images of their
profession that better reflect their responsibility to their publics and their beliefs about
their function as public servants in their communities.
6
The Ethic of Accuracy: Troubles in the Man-‐Machine Mix
By Jen Henderson
7
Abstract (150 Words)
By the 1960s, computer models had begun to improve the predictive accuracy, and
thus scientific legitimacy, of National Weather Service (NWS) forecasters. One operational
meteorologist, Leonard Snellman, conceptualized this optimistic collaboration between
humans and nonhumans as the “man-‐machine mix.” I argue that this image came to
represent forecasting scientists and enabled them to mount an ethic of accuracy, which
continues as a dominant value in their work. This scientific persona, however, relies on a
clear demarcation between humans and machines, one that is continually troubled by the
growing power of computer models. In the tradition of Foucault, I trace a genealogy of the
“man-‐machine mix” to demonstrate that anxieties emergent in its evolution, such as fear
over automation, reflect limitations of forming an identity around a single overriding ethic.
This image leaves out other ethics relevant to their practice that, as computer model
precision increases, relegate them to a future profession centered on communication,
narrowly defined as information transfer.
Keywords: Accuracy, weather forecasting, values, automation
8
The Ethic of Accuracy: Troubles in the Man-‐Machine Mix
Weather forecasting is evolving in a world characterized by accelerating scientific and technological change… [which] has led to some confusion and concern about the role of humans in forecasting the weather. -‐-‐Charles Doswell, “The Human Element in Weather Forecasting,” 1986
In early spring of 2016, I sat with meteorologists in a National Weather Service
(NWS) forecast office in the southeastern United States. They had been issuing flash flood
and tornado warnings for several hours within the boundaries of their county warning
area, the geopolitical space over which they have responsibility. The last storm,
represented as a splotchy mass of red and green pixels on a computer screen, had just
crossed the invisible boundary line into an adjoining office’s jurisdiction when we began to
talk about new software technologies being developed at the National Severe Storms
Laboratory, a weather prediction test bed in Norman, Oklahoma. They explained to me that
the lab is creating algorithms to help automate weather warnings. Eventually. “Eight to ten
years from now,” one of them emphasized.
“You mean you won’t be responsible for warnings?” I asked, surprised. “They’ll be
automated?”
A lead forecaster, Mark,55 laughed a bit. “Don’t say that too loud, Jen. That at some
point we won't be doing the warnings.” I had been conducting ethnographic observations of
forecasters in three other offices over the past year and knew well the importance of the
warning as an object of epistemological authority in NWS forecaster work. Warnings are
spatiotemporal alerts about dangerous weather that forecasters create at their
55 Names and identifying information has been changed to protect participants in my research.
9
workstations and distribute through their proprietary software systems. Their success
relies on notions of accuracy and timeliness on many fronts: accuracy of threat type, the
threat’s location and magnitude, as well as speed in detecting and constructing alerts
before such threats affect people. In sum, forecasters’ warnings generate knowledge about
risks to life that should, ideally, enable others to act, whether action comes from emergency
managers and other public safety officials, or members of their lay publics. Warnings, then,
help establish the societal relevance of NWS forecasters as scientists who successfully fulfill
their agency’s mission to “protect life and property.”56
References made in our exchange to the automation of daily weather forecasting
had become a familiar concern to me, one I’ve encountered a number of times when, during
shift work, operational meteorologists talk about the possibility of computer models
replacing the work of forecasters. It is a concern with a history, one that intensified in the
United States during the 1960s as people contemplated the role of machines in their lives.
With roots in the labor disputes that erupted across the country during World War II,
automation practices developed through technological advances and shop-‐floor politics
that pitted unions against management and put the control of the machine and the skill of
the worker in center stage.57 As historian David Noble notes of this entanglement, a
“shortage of skilled workers, engendered in part by automation itself, had now become the
supreme justification for more automation.”58 Automation produced a number of
ontological fears and professional anxieties. One writer in the 1960s, for example, proposed
that what people object to in automation is not so much the loss of labor but the change in 56 “Weather Ready Nation: NOAA’s National Weather Service Strategic Plan.” 57 For a thorough treatment of automation in the U.S., see Noble, Forces of Production: A Social History of Industrial Automation. 58 Ibid., 41.
10
the image of themselves. “There is a strong revulsion in many people against admitting the
possibility of machines behaving like human beings; they feel that this would be equal to
admitting that ‘man is a machine.’”59 For forecasters, such an idea cuts to the very heart of
their efforts to demonstrate their value to society as scientific experts.
In the local forecast office that day, news that the National Weather Service had
been developing mechanisms to automate warnings surprised me. “Warnings,” as one had
said to me, “are the last bastion of the forecaster” against the relentless precision of the
computer. I found myself repeating the question that most often accompanied such fears:
“So what would you all be doing?” I asked. What would the role of a weather forecaster be if
they are largely replaced in daily prediction by computer models and increasingly
outperformed in their accuracy and timeliness of warnings? In the parlance of the
forecasters, what “value added” might they contribute?
Mark looked at me and shrugged, “Essentially managing [the machines]. And
messaging.” Amid the talk of competition between humans and their technologies, then,
emerges a tension between the success of their work as predictive experts, which computer
models help facilitate, and the value of their own expert skill in the process. At stake are the
identities of forecasters as scientists and the survival of their profession in ways they
envision it ought to exist.
But there was one more element of this picture that I had been missing for me in
understanding their anxiety over professional loss. Scott, the forecaster sitting closest to
me, chimed in: “The man-‐machine mix is essentially what it's going to be.” The others
nodded in agreement.
59 Gabor, “Inventing the Future,” 142.
11
Scott explained the term “man-‐machine mix” as a “kind of artificial intelligence,”
which I would come to understand as a hybrid assemblage of mathematical equations,
scientific knowledge about the atmosphere, and technological infrastructures of the
computer living alongside human expertise, training, and experience. The concept sounded
like something out of 1950s science fiction, some futuristic vision of cyborgs, part human,
part machine. In Scott’s explanation, however, the image was much more pedestrian.
Some of the testing at the [Norman] test bed has found that machines and algorithms [do] a lot better than the human at more quickly identifying and tracking the hail cores. And it's about crunching numbers, it's that you know the environment… For example, there's always a trigger for us: What's going to produce quarter inch hail? The machine's already going to know that and it's going to see it before you see it. And it will track it and do it better than you can—more often, too.
Using a test bed to experiment with different combinations of humans and machines in the
warning process suggests that forecasters’ framing of the mix as competition with
computer models is not without merit. Findings from the test bed note the ways in which
one is better than the other on different registers, and conclusions about the different
elements—human, machine, man-‐machine—influence decisions about where forecasters
belong in their work.
Likewise, laboratory testing suggests a search for ways to clearly distinguish what,
in forecasting practice, is intimately entangled. In a forecasting office, boundaries between
human and computer are fluid, blurred, and multiple. There is no single human nor a
solitary machine but a plurality of both; there is no unidirectional process of humans and
machines but a folding of interactions whereby humans, for example, inscribe algorithms in
machines based on mechanized observational systems, which then generate several
computer models to produce an array of possible futures. Humans select from these
outcomes, integrating their possibilities with new observations taken from a variety of
12
machines, as well as their own experiences and judgments. Machines become a way to
visualize the atmosphere, meaning forecasters see through the eyes of those who created
the algorithms, their workstations, and their software. Together, these sociotechnical
arrangements create something that might be better characterized as a hybridity of
“humachine” assemblages. In the concept of the man-‐machine mix, then, the notion of a
“mix” is more plausible than a hyphenated adjective that suggests partitioning the two. So
why work so hard to keep man and machine separate?
Continual efforts made to demarcate humans from machines are important to
understanding the image of the forecaster as scientist, which is how forecasters envision
themselves as experts. As scientists, forecasters must meet the standards of good science
whereby their predictions can be verified and thus meet claims of accuracy. Yet, accuracy
itself is difficult to define since its meaning depends on one’s point of view and techniques
of definition used. What is accurate to the forecaster in terms of correctly labeling a threat-‐-‐
a tornado versus strong winds, for example-‐-‐might be meaningless to people who
experienced damage to their home. Accuracy for people in the community might mean how
quickly they got a warning that told them about the kind of danger they might expect. For
the purposes of this article, then, I define an ethic of accuracy as the value forecasters place
on correctly identifying unfolding meteorological conditions before weather phenomena
occurs and potentially effects people in their communities. It is a definition that reflects
common notions of accuracy found in the ways forecasters count accuracy in their metrics
of success and in their narratives of competition with computer models.60
60 Metrics of success dictated by the Government Performance and Reporting Act include false alarm rates, critical success index, and probabilities of detection.
13
Another important reason to demarcate humans and machines, then, is forecasters’
concerns over automation, or a continual threat about being replaced by computer models
that keeps forecasters in competition with the machines. “Automation,” Peter Drucker,
management scholar, wrote in 1962, “can be defined simply though superficially as the use
of machines to run machines.” But it is more than this, of course. Threats of automation also
limit the professional role of the forecaster. In one version of the future, forecasters could
see themselves as machines, whether as an extension of the machine managing them from
“over the loop,” or distilled as a representative knowledge base, separated from their
bodies and mechanically integrated with the machines. It is an image they have struggled
against for nearly forty years in their publications and practices. As more than one
forecaster has revealed to me, even today, many refuse to let the computer models make
their forecasts; they prefer instead to work out the forecast for every hour and every day
for which they are responsible on shift. “They’re still trying to make it their forecast,” one
member of NWS management staff noted. The problem to solve here is what a forecaster
ought to be and how to find possible ways forward that reflect the best of who they already
are. Yet, as my article will demonstrate, threats of machines outperforming humans
continue to overdetermine possible forecaster identities, ones that may not entirely reflect
forecaster practices or who they might become.
Meteorology, like other sciences, is a sociopolitical enterprise, and its imagery,
metaphors, processes, and languages hold within them insight into what scientists value.
Scholars in Science and Technology Studies (STS) have illustrated that instead of a value-‐
free and human directed enterprise, science is a social process in which many different
human and non-‐human actors have agency. Nor are the practices of science linear and
14
straightforward but complex, messy, historically contingent and politically motivated. Thus
it is with the man-‐machine mix and the ethic of accuracy. In his book Inventing Accuracy: A
Historical Sociology of Nuclear Missile Guidance, for example, Donald MacKenzie reveals the
social aspects of missile accuracy design and how it became “a product of a complex
process of conflict and collaboration between a range of social actors… that has fueled, and
has itself been fueled by, the cold war.”61 Accuracy in this context imparts the motives,
history, arguments, and everyday details of how nuclear guidance systems coproduced
nuclear society. The world of forecasting similarly offers a sociotechnical window into the
multiplicity of thinking about accuracy and its instantiations.
Phaedra Daipha writes most pointedly about this ethic’s circulation in operational
meteorology contexts in her book Masters of Uncertainty, noting that, “All [NWS]
organizational effort is directed toward improving the accuracy of NWS predictions.”62 She
explains how such a focus puts at odds agency directives that demand accountability for
performance standards and the pressures of public safety. She writes, “The essence of a
good forecast is currently distilled into two metrics: accuracy and timeliness,” which bears
out most often in the threat of severe weather. “Accuracy concerns,” however, “may be
silenced in the name of public service.”63 In other words, operational meteorology’s daily
activities get divided between those that justify forecasters’ scientific endeavor and others
that represent their dedication to serving society. Here, accuracy functions as a boundary
object64 that intensifies what the institution ought to count as success when lives are at
61 MacKenzie, Inventing Accuracy: A Historical Sociology of Nuclear Missile Guidance, 3. 62 Daipha, Masters of Uncertainty: Weather Forecasters and the Quest for Ground Truth, 117. 63 Ibid., 120. 64 Star and Griesemer, “Institutional Ecology, ‘Translations’ and Boundary Objects: Amateurs and Professionals in Berkeley’s Museum of Vertebrate Zoology, 1907-‐39.”
15
stake. This tension between forecasters’ scientific praxis and responsibility to the public is
where this article finds its purpose and it is here where the “man-‐machine mix” and its
ethic of accuracy becomes an important concept to understand.
In what follows I give a genealogical account of the “man-‐machine mix” and the
scientific persona of the forecaster it generates. I trace the evolution of this image to
demonstrate that anxieties evident today, in particular fears over automation, reflect the
limitations of forming an identity around a single overriding ethic. I argue that the “man-‐
machine mix” has kept the National Weather Service and its forecasters overly focused on
an ethic of accuracy and the sociotechnical apparatuses that serve such interests. This
scientized image of forecasting leaves out other ethics relevant to their practice that, as
computer model precision has increased, relegate them to a future profession primarily
centered on communication, narrowly defined as information transfer. In this endeavor I
follow scholars like Foucault who examined a history of the present through episodes in
the past. “A genealogy,” he writes, “must be sensitive to [the term’s] recurrence, not in
order to trace the gradual curve of their evolution, but to isolate the different scenes where
they engaged in different roles.”65 To this end, I examine three “scenes” where inflections of
the man-‐machine mix generated different roles for the forecaster as scientist.
I begin with the origin of the man-‐machine mix in meteorology when forecaster
Leonard Snellman coined the term in the late 1960s to capture the great optimism and
enthusiasm about the growth of operational forecasting as a science. Next I examine two
moments of significance for the man-‐machine mix: 1) the mid-‐1970s when the accuracy of
forecast accuracy unexpectedly declined leading to a potential loss of forecaster skill and
65 Foucault, Language, Counter-‐Memory, Practice: Selected Essays and Interviews, 140.
16
profession, which forecasters called “meteorological cancer;” and 2) the mid-‐1990s when
meteorologists grappled with how to identify forecaster value either by differentiating
from machines their unique contributions and value to users or integrating their judgment
and expertise fully into the machines. I show how these moments have led to a multiplicity
of current articulations of accuracy as they operate in forecasting discourse. I conclude by
suggesting that, in practice, forecasters have never been exemplified well by an exclusive
emphasis on the ethic of accuracy, nor should they continue to believe they have to be.
Man-‐Machine Mix as Optimistic Vision
In the mid-‐1950s a new machine called the electronic computer transformed the
profession of operational forecasting. Based on an agenda established by the Joint
Numerical Weather Prediction Unit in Washington, D.C., researchers developed “objective
analysis procedures,” or machine outputs, based on numerical weather prediction
methods. Together, these generated a prognosis of the synoptic scale, or large scale
features of the atmosphere, which statistical techniques then transformed into guidance for
forecasts.66 Generated at a central location, the National Meteorological Center in Suitland,
Maryland, prognosis maps based on computer model solutions were sent over teletype and
DIFAX to one of 254 local weather service forecast offices and weather service offices
66 Fawcett, “Six Years of Operational Numerical Weather Prediction”; Klein, “The Computer’s Role in Weather Forecasting”; Schuman, “History of Numerical Weather Predication Ad the National Meteorological Center.” For a more complete history of Numerical Weather Prediction and computer modeling in weather forecasting see Edwards, “Representing the Global Atmosphere: Computer Models, Data and Knowledge about Climate Change.” and Lynch, “The Origins of Computer Weather Prediction and Climate Modeling.”
17
across the United States. 67 Within their local communities, forecasters could then use this
guidance to assist them in making more specific predictions for their respective areas,
mainly about elements like temperature, cloud cover, wind speed, and precipitation.
Because of the speed and power of the computer, these computations could be
carried out at a rate that outpaced the unfolding of weather itself. For forecasters, it did so
on a scale of resolution and across specific enough variables that they could issue
predictions for a specific location in time and for particular elements of the weather with
increasing accuracy. This allowed them to meet the “presumed goal of each forecaster,”
one meteorologist wrote at the time, “to maximize his or her gain over the climatological
forecast,”68 or what they called skill.69 For example, by 1961, these techniques and special
models had increased the overall skill for five-‐day temperature forecasts. By the end of the
decade, computer models had became so ubiquitous in their practice that forecasters gave
a name to their relationship with it, one that clearly referenced an image of forecasting
science through the interplay of humans and computers.
The concept of the “man-‐machine mix,” as it was called, first appeared in a U. S.
Weather Bureau70 technical memo published in August 1969 by Leonard Snellman, then
67 Schuman, “History of Numerical Weather Predication Ad the National Meteorological Center”; Friday, “The Modernization and Associated Restructuring of the National Weather Service: An Overview.” 68 Bosart, “SUNYA Experimental Results in Forecasting Daily Temperature and Precipitation,” 1013. 69 Sanders, “Skill in Forecasting Daily Temperature and Precipitation: Some Experimental Results,” 1172. 70 The Weather Bureau would become the National Weather Service in 1970. For a history of the institution see Whitnah, A History of the United States Weather Bureau.and Hughes, Century of Weather Service: A History of the Birth and Growth of the National Weather Service 1870-‐1970..
18
Scientific Services Division Chief at the Salt Lake City regional forecast office.71 In it, he
articulated the “roles of man and machine in operational forecasting” and the direction he
saw for the field over the next decade. It was an exciting time. As other historians have
noted, computer processing power in the 1950s and 1960s created for the forecasting
profession an abundance of data about the atmosphere and a range of options for digitally
processing and using this information.72 To aid forecasters in sorting through their
growing bounty of information, much of the discussion at the Weather Bureau’s
headquarters involved which processes could be automated and which still needed the
intervention of the forecasters. Such decisions, one meteorologist wrote, depended on
“whether the amount of improvement obtained manually warrants the extra time required
to modify the machine product. The criterion is primarily one of accuracy versus time…”73
Thus, if a more precise forecast could be obtained by letting forecasters reconsider the
computer model’s output for a short period of time, then the product should not be
automated. Automation, then, offered one measure of forecaster performance.
Snellman formulated this relationship as a mathematical expression, distilling an
evolving relationship into a quantitative form: ( + Machine = Final Forecast. The
“man” in parenthesis, he wrote, signified forecasters at both a “major” center, like the
National Meteorological Center in Suitland, Maryland, and at a “local field station” in their
community. Working together as a team, the forecasters at the major center provided
machine, or computer, guidance to assist the local forecaster with daily predictions.
Together, the humans and the machines created a final forecast, or the “product” 71 Snellman, “Man-‐Machine Mix in Applied Weather Forecasting in the 1970’s.” 72 Edwards, A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming; Fleming, Fixing the Sky. 73 Klein, “The Computer’s Role in Weather Forecasting,” 196.
19
disseminated to various users in various text and numerical forms. A daily forecast, for
example, might call for partly cloudy skies with a 40% chance of rain and a high of 80
degrees.
Using a mathematical equation as an analogy for this collaboration suggests
Snellman intended the man-‐machine mix to maintain a certain balance between the
variables to the left and the outcome on the right. Together, the two could produce
something more “true” than either one could alone; that is, the sum of them together would
produce a forecast better than either alone. Yet, it would be this very interpretation of
variables, their ratio and meaning, that would change over time. So, too, would the product,
at times reflecting the forecast itself—its quality, speed, accuracy, etc. In other contexts, it
would reflect the forecasters, as though the product of the man-‐machine mix were not so
much the prediction but the profession.
This was true at the national level, as well. A few months later in October 1969,
another meteorologist wrote in a similar vein about the concept of the man-‐machine mix.
William Klein, of the Techniques Development Laboratory in Suitland, Maryland, noted in
his article that the “philosophy of the man-‐machine mix dominated” much of the process at
the National Meteorological Center. “This means,” he wrote, “that certain computer
forecasts… are transmitted directly over facsimile and teletype” based solely on machines,
while others that “draw on the man-‐machine mix … are first modified or “massaged” by
experienced forecasters at NMC before being issued to the field.”74 Being generated “solely
by the machines” was another way of saying these forecasts here had been automated, with
some oversight from the forecaster. The man-‐machine mix, however, relied on the
74 Ibid.
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forecaster to add value to the computer model guidance, thus improving it. Few products at
this time were fully automated, though Klein felt this would soon change.
Snellman’s vision of the man-‐machine mix retained the value of the forecaster as an
arbiter of information coming from the machine who had primary responsibility for the
needs of others. In the late 1960s, local operational offices used two machine products
generated by computer models. The first was “a prognostic product” that forecasters could
use as guidance that they could then “adapt” in ways that addressed the “versatile
requirements” of different local users.75 A forecast not usable by those it was intended to
inform, then, was deemed ineffective. Thus one identity of the forecaster in the initial intent
of this concept is one of public servant, someone who must learn about their users in order
to provide them the “best and most appropriate forecasts.”76 Keeping a balance between
the man and machine working together, he suggested, offered the promise of an accurate
and useful science.
Timeliness also emerged as a benefit of computer models that would be attributed
to the profession. An accurate forecast, after all, is useless to users if delivered too late.
Speed emerged as a advantage in the second product offered by the machine which were
“ready for direct use” to a particular user, such as pilots in aviation, who could not wait for
forecasts to be changed by the men in the office.77 Forecasters simply “communicated” this
information, by which Snellman meant simple information transfer, leaving them 75 Snellman, “Man-‐Machine Mix in Applied Weather Forecasting in the 1970’s,” 3–5. 76 Murphy, “What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting,” 282. 77 In one of only a few surveys conducted on gender in meteorology, a 1982 study found that 10% of bachelor degrees in meteorology at the time were awarded to women. In 1974, of 1,554 NWS employees, 20 were women, or 1.3% of the workforce. By 1980 that number had change to 39 of 1,338, or 3.7% of the workforce. See LeMone and Waukau, “Women in Meteorology.” for more information.
21
functioning as an intermediary between the machine and the user. The man-‐machine mix
in this example foregrounds benefits of the machine, namely speed, over human ability,
though forecasters still oversaw and thus bore responsibility for the process. In this sense,
machines helped forecasters confirm their own conclusions about future weather and did
so more efficiently since they could process more information more quickly.
The man-‐machine mix thus embodied the promise of a better science and an image
of the forecasters as legitimate scientists and servants to society. As one meteorologist
would later write of the advantages of the machine: “For the first time, meteorologists
could envision their science taking its place alongside certain select branches of physics as
a ‘hard’ predictive science.”78 Why did they not consider themselves to be true scientists
before the man-‐machine mix?
Until the machines, Snellman explained, forecasting had been a “laborious” and
“subjective” process conducted by a single individual who had to assimilate available
meteorological observations on hand-‐drawn synoptic maps, which offered a static picture
of the current state of the atmosphere. Sometimes referred to as the “art of forecasting,” a
term that still frequents weather forecasting discourse, many considered this earlier
process unscientific guesswork, in part, because of its inaccuracy and slowness. In this pre-‐
computer era, the “art of forecasting” created a foil to the “scientific forecasting”79 of later
years, a bifurcation that has roots in the 19th century when meteorologists debated the
appropriate work of their burgeoning science. As Katherine Anderson argues in her book
Predicting the Weather, many meteorologists believed observations and analysis, and not
prognostication, should be the focus of their enterprise. The latter, many feared, would cast 78 Doswell III, “The Human Element in Weather Forecasting,” 8. 79 Schaefer, “Severe Thunderstorm Forecasting: A Historical Perspective,” 164.
22
doubt on their position as scientists since they based their forecasts on subjective, meaning
wholly human, analysis, which was often wrong.80
Many of the processes and practices by which forecasters arrived at their prediction,
the how of forecasting, likewise changed in the man-‐machine mix. With computer models
came the possibility of two kinds of techniques: subjective (human) versus objective
(machine) forecasting. Much of the literature published during the 1960s and 1970s
highlights the creation of an “objective” forecast guidance that might complement, offset,
and even replace the “subjective” human processes. For example, in their summary of
1970s weather forecast verification, Nap et al81 note the problematic as a central feature of
forecasting to be overcome: “Although objective tools are used [in forecasting methods],
the final forecast is subjective rather than objective” since the human still has the choice to
base their forecast on the machine guidance or not. The consequences of subjectivity, the
authors argued, were great. “This means that a forecast made by A will not be exactly
reproduced by an independent forecaster B and in many cases it is difficult to describe how
the forecast is made.”82 Humans, that is, create forecasts that are unreliable and thus
potentially invalid.
Subjectivity can be seen as a professional failure of a kind of “aperspectival”
accuracy,83 a reliability that sits at the heart of forecasters’ longstanding concern over their
profession. From an agency perspective, “taming of human subjectivity”84 orders the world
for both investigation and for administration. From a forecaster perspective, the machine
80 Anderson, Predicting the Weather: Victorians and the Science of Meteorology. 81 “A Verification of Monthly Weather Forecasts in the Seventies.” 82 Ibid., 306. 83 Daston, “Objectivity and the Escape from Perspective”; Daston and Galison, Objectivity. 84 Porter, Trust in Numbers: The Pursuit of Objectivity in Science and Public Life, 21.
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in the mix offered forecasters a language to describe a scientific persona that then linked
other principles of science—validity, reliability, objectivity—with their enterprise. In
optimistic tones, both Klein and Snellman agreed on the short-‐term future outcome of
forecasting in light of the man-‐machine mix. They envisioned a time not long in the future
when forecasters might be eliminated from some of the more laborious and routine aspects
of prediction. Yet in the long run, they differed substantially both on the man-‐machine mix
and the ideal of their science.
Snellman believed that computer models would eliminate the need for forecasters
to create guidance products at a national center, but the local forecaster would “remain
paramount” in “adapting it to meet local area user requirements,” especially in the
preparation of near term, specialized forecasts. He wrote, “This service-‐oriented role of the
meteorologist—if he is trained both psychologically and academically for this job—should
be challenging and rewarding.”85 In his model, emphasis on the users, or those who receive
the forecasts and act on forecasts and warnings, takes a central position in the profession
and contributes to the legitimacy of operational meteorology as a fully fledged science.
Snellman saw humans and machines as entangled in ways that suggested they could
not be separated without harming their science. Even in this earliest of valences of the
man-‐machine mix, one possible forecaster identity is both a scientific expert capable of
accurate and timely predictions and a public servant vested in the local situatedness of
individuals dependent on their expertise and advice. Accuracy and service—being right
about predictions but also being concerned about people and their needs—together are
85 “Man-‐Machine Mix in Applied Weather Forecasting in the 1970’s,” 7.
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entangled and constitute a good a forecast and thus a good forecaster.”86 Humans could not
be eliminated from the mix, nor could they be automated out of the image of their
profession.
Klein saw things differently. He conceptualized the subjective nature of human
forecasting as an obstacle to the fulfillment of forecasting science. He wrote that in the
short term, “the concept of the ‘man-‐machine mix’ will probably predominate at local
forecast offices” allowing humans to participate as an important element of forecasting.
This was only true, however, “until the decade of the 1990’s” when he suspected models
would outperform humans in both accuracy and speed. “By the turn of the century,” he
wrote, “all aspects of weather forecasting should be automated, and the long evolution
from subjective to objective forecasting will be completed.”87 This more purified vision of
forecasting science saw humans as the source of bias and error, flaws that might keep their
profession from realizing its ultimate goal of perfect forecasts.
The teleology of Klein’s normative vision for forecasting presented a starker future
for forecasters. Humans might still be involved in the design of the machines and writing of
the algorithms, or humans might still oversee the machines as they produced their
products, but they would not be scientists. Klein does not propose an alternative for
forecasters as experts; instead he suggests a vision of forecasting science in which forecasts
are completely automated and humans are disappeared. In effect, forecasters are happily
automated out of their jobs because their science is better executed by machines. At the
moment of its origination the language of the man-‐machine mix hinted at the multiplicity of
possible futures for the identity of the forecaster. 86 Ibid., 6. 87 Klein, “The Computer’s Role in Weather Forecasting,” 202.
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Regardless of the unsettled nature of forecasters in the future, the consensus at the
time framed the man-‐machine mix, and their science, optimistically. In 1978, George
Cressman, former director for the Joint Numerical Weather Prediction Unit, which created
the first computer models used in prediction, noted, “Weather forecasting is coming of age
and achieves the solution of problems every bit as difficult as those solved by other
scientific and engineering professions.”88 The machines had helped propel weather
prediction into the ranks scientific authority, making possible a profession that had once
been called weather prophecy.89 In fact, the 1970s represented a dramatic transformation
of weather forecasting as a scientific endeavor for those in its employ.
Earl Drossler, then Commissioner for the American Meteorological Society’s
Scientific and Technological Activities Commission, published a retrospective analysis of
the events and activities of note during the 1970s in which he highlighted several
contributing factors to their growing success as a profession. Operational forecasters, like
those in the National Weather Service, had arisen from relative obscurity in the pages of
prestigious research journals, such as the Bulletin of the American Meteorological Society,
one of the most respected in its field even today.90 He explained that during the 1960s,
fewer than a dozen articles appeared about weather forecasting and even fewer were
written by operational meteorologists. By 1978 that number had tripled to 34—“an
average of almost three articles per issue.” Additional signs of professional success
emerged. Meteorological conferences and workshops began to offer forecasters a platform
to discuss issues relevant to their profession, and with a renewed sense of purpose and 88 Droessler, “The Weather Forecaster Today,” 195. 89 Anderson, Predicting the Weather: Victorians and the Science of Meteorology. 90 Impact factor of BAMS, 7.929, according to the journal website found at the following url: http://journals.ametsoc.org/toc/bams/current.
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growing success with forecasts for major national weather events, forecasters formed their
own professional society, the National Weather Association, in 1975.91 It seemed the
science of forecasting had begun to hit its stride.
Yet, almost as soon as they made their hopeful pronouncements, others began to see
problems in the man-‐machine mix that might harm their expertise. Snellman’s and Klein’s
optimism about the future of their predictive enterprise would be challenged by the
practices of forecasters themselves. Computer models improved yet forecast accuracy
declined. Why? The man-‐machine mix was not just about the two entities—human and
nonhuman—working in tandem; they had a context, a social import, and a growing ethical
complexity. This can best seen in the ways the connotation of the man-‐machine mix would
change, shifting from a primarily optimistic and forward looking language of the forecaster
to one of anxiety and fear over a potential loss of their profession.
Man-‐Machine Mix as Disease: Meteorological Cancer
By the early 1970s, the meteorological community had begun to sound an alarm.
Although there had been significant improvements on numerical models and statistical
methods, forecasters did not continue to see commensurate improvements in their daily
predictions. A National Science Board report in 1972 noted that “Although of great
economic benefit, present day forecasts fall well short of perfection.”92 This was true at
almost all scales of weather prediction: short range (0-‐24 hours), medium range (1-‐5 days),
and long range (beyond 5 days, including seasonal forecasts).93 The report explained the
problem on many fronts. First of all, the numerical models used by the National
91 Harned, “NWA History.” 92 National Science Board, “Patterns and Perspectives in Environmental Science,” 93. 93 Ibid.
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Meteorological Center in their guidance products were best at creating prognoses for the
synoptic, or large-‐scale, motion of the atmosphere. Elements such as fronts and jet streams,
for example, fared better in the models than smaller, or mesoscale, features, such as
precipitation. Part of the issue was with the sparse observational network, an important
element of the computer model in that observations provided its initial conditions. Thus,
only more and denser observations would offer significant improvements. Further, these
synoptic models “smoothed out minor irregularities” and thus eliminated mechanisms in
the atmosphere, such as turbulence, that might be partly responsible for the initiation of
storms. Some suggested that the weather community should slow its efforts in computer
modeling and spend more time collecting observations on these little understood
processes.94
Another problem involved quantification. Statistical equations interpolated output
from these numerical models into possible forecasts. However, the variety of equations that
had been developed over the years for different weather phenomena raised questions
about which ones were best and which should be the standard in the National Weather
Service.95 Model Output Statistics, or MOS, were promising and were widely used; yet they
only covered a few weather elements, such as temperature and precipitation.96 And other
statistical measures needed development so that they could more comprehensively track
the skill of forecasters in making improvements (or not) over the years. Based on these
issues, some in the forecasting community worried that perhaps they had “plateaued” in
94 Ramage, “Prognosis for Weather Forecasting,” 6–7. 95 Glahn, “On MOS and Perfect Prog for Interpretive Guidance”; Klein, “Objective Forecasts of Surface Temperature from One to Three Days in Advance”; Wassall, “A Study of the Significance of Forecaster Changes to MOS Guidance.” 96 Glahn, “Progress in the Automation of Public Weather Forecasts.”
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their accuracy given the current state of the science and so were in need of a
“breakthrough.”97
A final problem in the man-‐machine mix of the 1970s lay in developing methods for
the verification of forecasts. While the forecasting community created processes and
technologies with the goal of achieving improved accuracy and timeliness of forecasts, no
database existed that could adequately archive forecasts or their verified outcomes.98 The
profession of forecasting, one forecaster wrote, “is sorely in need of a quantitative basis for
appraising present forecast skill, to say nothing of its variation with element and with
location…”.99 Proving the trends in forecast skill had become a major concern.
In 1973, one meteorology department had collected enough data in at
Massachusetts Institute of Technology over six years to analyze forecasting skill of
university meteorologists and compare their outcomes with official forecasts. What they
found shocked them. Their skill in forecasting showed little improvement in most areas
and a decrease in accuracy in others:
…perhaps the most striking and sobering result is the lack of systematic increase in forecast skill over the last six years. In fact, our skill in precipitation forecasting has shown a slight downward trend, an experience which seems to have been shared by forecasters at the NMC.100
Not only did this study demonstrate that forecasters struggle with accuracy of forecasts for
basic elements of daily prediction (temperature and precipitation) but their results
97 Bosart, “SUNYA Experimental Results in Forecasting Daily Temperature and Precipitation”; National Science Board, “Patterns and Perspectives in Environmental Science”; Ramage, “Prognosis for Weather Forecasting”; Sanders, “Skill in Forecasting Daily Temperature and Precipitation: Some Experimental Results.” 98 Sanders, “Trends in Skill of Daily Forecasts of Temperature and Precipitation, 1966-‐78.” 99 Sanders, “Skill in Forecasting Daily Temperature and Precipitation: Some Experimental Results,” 1177. 100 Ibid.
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validated a surprising fact that the National Meteorological Center likewise suffered. Other
studies confirmed these results.101 It appeared that forecasters had lost ground they
thought they had won in the battle to develop an ethic of accuracy.
What could account for this loss? Leonard Snellman had a theory and a name for the
problem. In 1977 he published his concerns about the man-‐machine mix in an article,
“Operational Forecasting Using Automated Guidance.” In it, he cautioned operational
forecasters about a particularly virulent threat to their profession. He argued that many
had begun to accept too easily model solutions and simply repackage and forward the
forecast unchanged. Snellman worried they were losing themselves, and their accuracy, to
the machines: “Forecasters,” he wrote, “are relinquishing their meteorological input into
the operational product going to the user.” Instead, they were “operating more as
communicators and less as meteorologists.”102 Importantly, communication in this instance
did not mean an open dialogue with users. Instead, he saw them as mere information
transfer points.
In an image that accompanied his article, he illustrated the idea of communication
(see Figure 1).103 On the left side of the image is a square machine representing the “NMC,”
or national computer. Out of a slot below a bunch of dials comes a piece of paper, meant to
represent the forecast. A series of unidirectional arrows show the paper traveling without
stopping into one the hands of a figure representing the forecaster, a figure with his back to
the reader. From there, the paper moves at the same pace into the hands of another figure
101 Brown and Fawcett, “Use of Numerical Guidance and the National Weather Service’s National Meteorological Center”; Jensen, “A Review of Federal Meteorological Programs for Fiscal Years 1965-‐1975.” 102 Snellman, “Operational Forecasting Using Automated Guidance.” 103 Ibid., 1043. ©American Meteorological Society. Used with permission.
30
who faces the computer at the other side of the image. This person is labeled “User
product.” Here the process stops. The image suggests that the forecaster changed nothing
about the information coming from the machine. In fact, it calls into question the need for
that forecaster as facilitator of the process in the first place. Take away the forecaster and
you have an image of automation, the machine working without human intervention until
the product is delivered. Here, the forecaster is simply an extension of the machine.
Less than a decade before, Snellman had written optimistically about the man-‐
machine mix and the value of forecasters, both in their expertise in adjudicating guidance
and in the way they tailored this guidance for their various users. He had noted a certain
pride and satisfaction that he felt his colleagues should derive from such service. Now,
however, a narrowly conceived form of communication, mainly as information transfer,
threatened to overshadow their science in harmful, even destructive, ways.
Figure 1 Meteorological cancer of the man-‐machine mix according to Snellman
Snellman gave this kind of communication a name that invoked a terminal disease.
“Since this practice is increasing slowly with time,” he wrote, “it can be called
©American Meteorological Society. Used with permission.
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‘meteorological cancer.’”104 Framed by the metaphor that erodes and diminishes its victim
on multiple fronts, Snellman invoked as the source for his professions’ newest concern an
overreliance on those same valuable machines that had demonstrated such import to their
success as predictive experts just a few years earlier. By simply transmitting the machine’s
expertise, they had diminished their own. The man-‐machine mix, in effect, weighted too
much toward the machine; it had lost its balance and both forecasts and their profession
might suffer as a result.
Forecasters like Snellman found themselves “between a rock and a hard place” in
proposing a solution.105 Some believed it was important to continue developing objective
guidance to fully realize its potential. As one forecaster wrote, “In my view we have every
reason to be optimistic that weather forecasting will continue to advance. To begin with,
the full potentialities of numerical weather prediction are far from being realized.”106
Others wondered about what this guidance would mean for them as professionals. They
began to recognize the potential of this objective guidance to “[destroy] the meteorologist’s
significant input” in the process. Just what ought to be the “proper roles of man and
machine” wasn’t obviously clear.107 At the heart of this concern lay the possibility of
automation such that that the National Weather Service would simply use “machines to run
machines,” a common definition of automation at the time.108 These machines could
translate the forecast for different users and leave humans to manage the more
104 Ibid. 105 Ibid., 1036. 106 Reed, “Bjerknes Memorial Lecture: The Development and Status of Modern Weather Prediction,” 398. 107 Snellman, “Operational Forecasting Using Automated Guidance,” 1036. 108 Drucker, “The Promise of Automation,” 222.
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bureaucratic aspects of prediction—that is, it would leave them “over the loop.”109
Snellman feared this practice was becoming the norm in operational settings and that the
quality of forecasts—and the value of the forecaster—would suffer from this ill calibrated
“man-‐machine mix.”110
His perspective resonated with others in the operational community, reinforcing the
notion that automation, loss of their profession, and computer guidance were all intimately
intertwined. One study about trends in forecasters skill, for example, noted a “widespread
concern that the automated prediction may replace much of the judgment part of the
forecaster’s job.” It likewise acknowledged “Snellman’s recent concern that the forecaster is
relying too heavily on the guidance (thus reinforcing the replacement in a kind of circular
process).”111 Interestingly, the reason for the replacement, the study suggested, was not
just the benign implementation of technology but the forecasters’ ways of engaging with
the machines. And it validated other work that pointed out the declining skill of forecasts.
Did this deference to computer models indicate that forecasters had trouble trusting
themselves as experts? Were they overwhelmed and so making choices based out of
frustration or lack of time? One thing would become clear, this “cancer” as Snellman called
it, created other effects in the forecaster community as this “widespread concern” filtered
into research studies, office culture, and even testimony given to Congress about the role of
the human amid such rapid technological change.
Over the next few years the man-‐machine mix and threats of automation created a
tension for administrators in explaining the benefits of a more efficient system, perhaps at 109 LeFebvre, Development of AWIPS. 110 Snellman, “Operational Forecasting Using Automated Guidance.” 111 Sanders, “Trends in Skill of Daily Forecasts of Temperature and Precipitation, 1966-‐78,” 766.
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the expense of the workforce. In 1977, the same year as Snellman’s seminal publication, Dr.
George Cressman, director of the National Weather Service, gave testimony to the House of
Representatives Committee on Science and Technology. He highlighted for the panel the
various kinds of automation that had been a boon to forecasters thus far. A new
development, for example, called computer-‐worded forecasts, would “at the push of a
button” generate the language that forecasters had traditionally used to manually describe
their predictions. Instead of writing their text based on individual judgment and stylistic
preferences, forecasters now had “limited flexibility” in selecting between forecasting
conditions—wind, temperature, cloud, and precipitation—as well as the time period for the
phenomena and the order of the phrasing.112 In effect, the machines would now speak for
the forecasters.
Cressman likewise addressed the alarm forecasters had expressed within the
agency about the consequences of improved machines. Their agency’s efforts, he said, were
…sometimes misunderstood as an intention on our part to replace man by machine. That is not the case. The idea of presenting the computer-‐worded forecast to the forecaster is to give him a starting point, to save him a lot of his preliminary thinking, to let him really concentrate on the really difficult issues at hand, and give him a forecast, that he can amend, wipe out, or redo as he sees fit.113
Framed as an issue of efficiency that could bring forecaster skill into relief as they attended
to the more challenging elements of their work reintegrated man and machine, coupling
them to elevate their profession. But this framing did little to assuage forecasters’ anxieties
over the future of their profession.
Many felt demoralized. In written responses from the National Weather Service to
questions posed in Congressional hearings a year later, administrators noted the following. 112 Glahn, “Computer Worded Forecasts,” 6. 113 Cressman, Briefing on the National Oceanic and Atmospheric Administration, 168.
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“While the overall morale of Weather Service field personnel cannot be said to be low, it is
not particularly high either.”114 The threats of automation had taken a toll on forecasters
who worried about their jobs, even as it affected their motivation. “Ironically,” they
admitted, “the increased use of computer products and its threat to the human workforce
has tended to reduce some individuals' incentive to put out the best possible product.”115
Rather than being seen as an issue of the professions’ continual effort to keep separated
what clearly required both humans and machines to be successful, the issue became the
forecasters and their poor reaction to automation. Still, administrators acknowledged that
automation potentially had negative impacts for the quality of their forecasts for others,
too. “The increased use of mass dissemination methods and the lack of travel have tended
to shelter the forecaster, cutting him off from the vital feedback as to users' needs and the
consequences of the forecasts.”116 Mediated communication strained forecaster
interactions with their users, raising questions Snellman first addressed in his 1969 paper
about what constitutes a good forecast and thus a good forecaster.
Finally, concerns about being replaced reached the heads of the National Weather
Service’s parent organization, the National Oceanic and Atmospheric Administration. In
1978, Richard Hallgren, acting assistant administrator to NOAA, made a point of reassuring
Congress of the value of the human in the mix amid technological change in testimony
given to the House Subcommittee on Transportation, Aviation, and Weather. He discussed
the limited promises of digital technologies, like radar, to provide predictions without
114 National Weather Service Act of 1978, 37. 115 Ibid. 116 Ibid.
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human intervention. The committee chairman offered a clarifying remark about the value
of automated, digitized technologies in the forecast and warning process. Hallgren replied:
I would not be prepared to say that we should take the forecaster out of the loop…. I would take the view we automate just as many of the functions [of the forecasting process] as we can… I think there are a sufficient number of uncertainties even with very advanced technology that one should not take him out of the loop entirely.117
Forecasters, it seemed, provided a reassuring presence given the experimental nature of
some changes. The machines that provided new possibilities needed forecasters to watch
over the machines should something go wrong. But just how the forecaster would
participate in the automated loop more broadly, however, was unclear. Would the
forecaster be “over the loop,” as in Snellman’s image, mere managers of the computer
process, providing quality control? Or would they continue to be embedded “in the loop” in
more meaningful ways? Or could there be multiple intersecting loops: loops of automation,
of forecaster expertise, of user interaction? If forecasters were in the loop but not making
predictions, then forecasters had become something else. That is, if the machine became
the predictors, what role was left for humans? This question would take prominence in the
1990s, as I will show shortly.
There was, however, a potential silver lining for operational meteorologists. Part of
the movement to automate “as much of the forecasting process as possible” included the
development of a forecaster workstation, called Automated Field Operations and Services,
or AFOS. First proposed in 1973 by National Weather Service administration, AFOS
functioned as a communications technology that would allow humans to receive and
transmit information quickly, instead of using more outdated and much slower tools,
117 Ibid., 23.
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DIFAX and teletype.118 The digital connection between the computers installed at the
national and local levels also meant forecasters could speed up their own evaluation and
assessment of information and their dissemination of forecasts to their users. The
microcomputer, in effect, allowed them to be more computer-‐like in their speed and
productivity and enhance their expertise in diagnosing the weather. It also made
forecasters and their machines more significant in the warning process: “AFOS,” one
meteorologist wrote, “will shorten the time between the recognition of hazardous weather
and the issuance of warnings to the general public to a minute or two at most… [T]his time
compression can make the difference between life and death.”119
Snellman saw this machine as a potential ally to the forecasters, one that might
assist them in rebalancing the mix. It would situate the forecaster in the machine and the
machine in the forecaster, diminishing the chance that humans could be eliminated from
the mix. Return for a moment to that image of meteorological cancer from Snellman (see
Figure 2).120 The bottom half of the graphic reveals the possible alternative to a future of
irrelevance. Here the same machine, forecaster, and user appear as with the model of
meteorological cancer; however, a new actor joins them, a minicomputer that faces the
forecaster. The machines in this image now each have different paths they can choose for
their information. The national computer can send information to the local minicomputer,
which then sends it on as a user product with little intervention. Or the national computer
can send the information to the forecaster. Important in this new graphic, however, is the
118 Lehmann, “AFOS: The AFOS Working Environment.” 119 U.S. Department of Commerce, “Program Development Plan: Automation of Field Operations and Services,” 4. 120 Snellman, “Operational Forecasting Using Automated Guidance,” 1043. ©American Meteorological Society. Used with permission.
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choice given to the forecaster, who may elect to use the local minicomputer to send
information to the user product or simply pass information directly to it. Only the
forecaster decides what happens to the information, though only the national computer
actually generates it to begin with.
The compromise here makes both machine and human not only equally important
but mutually imbricated in a forecast such that the user product is not delivered without
both working together. That is, they function as a cyborg assemblage. And as Haraway
notes of cyborg worlds, this is “about lived social and bodily realities in which people are
not afraid of their joint kinship with animals and machines, not afraid of permanently
partial identities and contradictory standpoints.”121 It is an image of scientific legitimacy
that “challenges dualisms” about the world even as it makes unclear “who is made in the
relation between human and machine.”122 Perhaps something that challenges not only the
boundaries between human and nonhuman but between experts and publics, or as the
graphic below suggests, between forecasters and their users.
But for the time being, Snellman believed AFOS kept forecasters on par with
machines, restoring balance to the equation of the man-‐machine mix. “AFOS with its great
advantages,” he wrote, “mostly the local minicomputer, will give the forecaster greater
latitude in using his meteorological knowledge, thereby improving operational
forecasts.”123 It allowed them to individualize their workstations such that they could select
and save a set of procedures, or data screens, unique to their preferences. The software
would allow “each forecaster to store a set sequence of alphanumerics and graphics to 121 Haraway, “A Manifesto for Cyborgs: Science, Technology, and Socialist Feminism in the 1980s,” 196. 122 Ibid., 219. 123 Snellman, “Operational Forecasting Using Automated Guidance,” 1044.
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appear on screen,” meaning they could “designate which screens the products will appear
on, which fields will appear as overlays, and the order of display.”124 Creating a
workstation, then, allowed the machine to become an extension of forecasters—not the
other way around—reproducing at greater speeds and in integrated ways their
preferences, expertise, and assessments of the weather of the day. AFOS seemed to offer a
way to rebalance man and machine by bolstering the forecasters’ contributions and helping
them add value to computer model guidance once again.
Figure 2 Two forecaster roles as envisioned by Snellman. Top: Meteorological cancer of the man-‐machine mix. Bottom: Rebalanced man-‐machine mix with AFOS.
Although the concern expressed over the replacement of forecasters by computer
models often appears in meteorological papers and conference publications as a concern
124 Lehmann, “AFOS: The AFOS Working Environment,” 3.
©American Meteorological Society. Used with permission.
39
over accuracy and employment, it was also about the loss of a phenomenological
experience of forecasting—a loss of a personal expertise and scientific profession
meteorologists had struggled to build for nearly a century.125 Arising from such
developments, however, came questions centered on how to value of the human element in
forecasting within the man-‐machine mix.
What Remains: Articulating the Human Element the Mix
On October 29, 1992, President George Herbert Walker Bush signed into law the
“The Weather Service Modernization Act”126 This bill authorized Congress to spend $4.5
billion over ten years to produce three technologies in an initiative called Modernization
and Associated Restructuring Demonstration, or Modernization: the WSR-‐88D, or Doppler
radar, which would replace the aging World War II radar network; the Automated Surface
Observing System to measure real-‐time atmospheric conditions at airports across the
country; and the Advanced Weather Interactive Processing System (AWIPS), a new
forecaster workstation that replaced AFOS with an “advanced computer and
communications system to help forecasters integrate, visualize, and analyze all sources of
weather data”127 The last piece of the initiative reorganized 122 local weather forecast
offices across the U.S. and re-‐trained forecasters on new technologies and policies.
125 Anderson, Predicting the Weather: Victorians and the Science of Meteorology; Friedman, Appropriating the Weather: Vilhelm Bjerknes and the Modern Construction of a Modern Meteorology; Nebeker, Calculating the Weather: Meteorology in the 20th Century. 126 The Weather Service Modernization Act of 1992. 127 National Oceanic and Atmospheric Administration, “National Implementation Plan for the Modernization and Associated Restructuring of the National Weather Service”; Friday, “The Modernization and Associated Restructuring of the National Weather Service: An Overview”; Select Committee on the National Weather Service, “Committee on National Weather Service Modernization.”
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Although AFOS had been implemented in the late 1970s, by 1981, a Comptroller
General report to Congress noted the program was struggling. It was five years behind
schedule and $100 million over budget.128 Even worse, design flaws plagued AFOS, such as
an inability to transmit radar and satellite data, which left it less useful to local field offices.
NWS administrators defended their agency by arguing that an effort to create automated
systems of this size and magnitude requires large-‐scale organizational and management
changes, something the agency had not adequately anticipated. “The development of large,
complex systems that break frontiers should be expected to encounter some problems,”
they wrote in response to the inquiry.”129 The Comptroller’s office agreed:
It should also be noted that NWS attempted to develop one of the largest distributed database systems ever designed, in-‐house, without trained ADP personnel, without increasing staffing levels, and without modifying its organizational structure. Given these constraints, management and technical problems are not surprising.130
In the end, a new machine would need to be developed, one that would have the same goal
of AFOS—to improve forecasting and warning efficiency through managed automation. But
the next system would be different, too. Importantly, it would be created within a larger
initiative to upgrade several elements of the weather warning system; and it would be done
in concert with the advice and insight of the forecasters themselves.
Changes in Modernization technologies reflected modifications to the ways
forecasters would work. Instead of typing out text-‐based forecasts designated for different
geographic zones, for example, the new system, AWIPS, would allow forecasters to explore
animated graphical representations of weather features and overlay them in ways they had
128 Comptroller General of the United States, “Problems Plague National Weather Service ADP System,” i–v. 129 Ibid., 73. 130 Ibid., 75.
41
never seen before. This new software and hardware system also enabled forecasters to
visualize the weather temporally and spatially, and in real time. Prior to AWIPS and its
forerunners like AFOS, forecasting technologies, such as radar and satellite, were
distributed throughout an office, often out of sight of forecaster issuing warnings. This
decentralized process for generating forecasts and warnings likewise meant that data itself
were integrated within the minds of individual forecasters, cognitively stitched together
without the benefit of overlapping temporal and spatial scales or the ability to step
phenomena through time.131 AWIPS took advantage of innovations in graphical computer
displays developed specifically for forecasters that generated dimensional representations
of atmospheric variables, enabling forecasters to newly “see” the hazards they would warn
for. Although it was designed, in part, to help forecasters newly see representations of the
weather, it still required them to decide between the hundreds of types of data just which
are more reliable, believable, and helpful in a given weather context. AWIPS, then,
facilitated information overload and potentially created an opportunity for meteorological
cancer—forecasters deferring to models—to continue to grow.
Snellman intervened again. He participated on many of the National Academy of
Sciences committee reports overseeing Modernization, helping facilitate the
implementation of AFOS and after his retirement, AWIPS.132 In 1982, he had developed
what he called a “Forecast Funnel” that offered a method of atmospheric diagnosis for
forecasters that enable a focused understanding of local weather. Starting with the
hemispheric level and working toward the synoptic level, forecasters gradually assessed
131 LeFebvre, Development of AWIPS. 132 MacDonald, “Leonard W. Snellman, 1920-‐1999.”
42
each scale of the atmosphere to better forecast what he called “the problem of the day.”133
His approach became a standard in forecasting practice, one still taught today. Combining
his diagnostic method with the efficiency and complexity of AWIPS kept the forecaster from
falling prey to meteorological cancer, just as AFOS should have.
But AWIPS also embedded forecaster expertise in the machine much more explicitly.
Developed as an iterative process between computer scientists and meteorologists at the
Program for Regional Observing and Forecasting Services and (later called the Forecast
Systems Lab) in Boulder, Colorado, and the National Weather Service office in Denver, the
functionality and graphical interface of AWIPS underwent significant user testing.134 This
allowed forecasters to co-‐design and co-‐envision how the “forecast office of the future”
might materialize as part of their daily practice. Every six months during the development
of the new workstation and software, forecasters were invited to sit side-‐by-‐side with
meteorologists at PROFS, helping them understand their preferences and procedures.
Among forecasters, AWIPS would resituate forecasters as experts in the process, allowing
them to participate in the building of the computer system that would work with them to
facilitate their skill. It was a system tailored to forecasters’ knowledge, built, as it were to
both capture and reflect the human. This would be a theme that developed throughout the
1990s as forecasters reconfigured ways of knowing the human in the mix.
Two main ideas about the role and identity of the forecaster arose during the 1990s
and share many of the same assumptions. The first argues that humans and machines have
133 Snellman, “Impact of AFOS on Operational Forecasting.” 134 National Weather Service Modernization Committee, “An Assessment of the Advanced Weather Interactive Processing System: Operational Test and Evaluation of the First System Build.”
43
two different knowledge domains that complement one another.135 Humans exhibit
judgment, which stems from their experience, contextual knowledge of the environment in
which they work, and the meaning they derive from their assessments. Their decision-‐
making abilities distinguish them from the machines, which are able to produce forecasts
expediently and with greater accuracy than the human yet they still make mistakes.
Because of their automation and speed, such mistake exponentially compound if not caught
early enough. Thus, many argued, the two used together offset each other’s weaknesses
and provide better forecasts than any one alone might. However, to best facilitate this
merger, some suggested importing the human element into the machine through some
mechanized, statistical process. The economist Harvey Stern wrote in 1993 that he
believed
the only way to preserve forecasters’ valuable domain and contextual knowledge as an integral component of the forecasting process, while simultaneously incorporating automated forecasting guidance into that process, may therefore be to utilize a system that mechanically combines the automated and human predictions.136
This view instrumentalizes the forecasters, making them simply a part of the
machine. Not unlike some efforts discussed in the context of automation decades earlier,
one such industry executive noted that “the more we automate the more we need to know
what makes the human being tick.”137 The man-‐machine mix, combines the knowledge of
both together but within the system of the machine, effectively making forecasters valuable 135 Stewart et al., “Analysis of Expert Judgment in a Hail Forecasting Experiment”; Lusk et al., “Judgment and Decision Making in Dynamic Tasks: The Case of Forecasting and Microburst”; Stewart, Roebber, and Bosart, “The Importance of the Task in Analyzing Expert Judgment.” 136 Stern, “The Future Role of Humans in the Weather Forecasting Process – to Provide Input to a System That Mechanically Integrates Judgmental (Human) and Automated Predictions?,” 2. 137 Noble, Forces of Production: A Social History of Industrial Automation, 39.
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only if (or until) it becomes possible to fully transfer human decision making into machine
decision making. Thus, it preserves the human temporarily and makes them the object of
study in the interim as researchers attempt to find ways to optimize the man and machine
to generate a “lift” in accuracy statistics. More important than accuracy, some noted that a
mechanized combination of human and machine would make forecasts more consistent
day to day and thus more valuable to those arranging their lives around weather
information.138 Even if not exactly correct, consistent forecasts allow for the planning
necessary to manage industries based on forecasts. A new dimension of political economy
emerges in this arrangement, appraising forecasters’ knowledge as an element of financial
import in the larger mechanized system.
The second view is much more generous, though it bifurcates the ultimate role of
the forecaster even as it preserves the human in much more meaningful ways. Instead of
looking for ways to enhance the man-‐machine mix itself, some took the view of dividing the
forecasting landscape, giving machines precedence over that which they do best: the day-‐
to-‐day forecasting. Since forecasters struggled to “routinely improve upon the accuracy”139
of predictions, meteorologists looked for alternative roles they might play that were
equally important and appropriate to their professional identity. The first role suggested by
authors Brooks and Doswell shifted forecasters out of their regular operations and situated
them as authorities over warnings issued to protect people. After all, they write, “it is
important to note that protection of life and property is, in some sense, the hardest
138 Stern, “The Future Role of Humans in the Weather Forecasting Process – to Provide Input to a System That Mechanically Integrates Judgmental (Human) and Automated Predictions?,” 6. 139 Brooks, Fritsch, and Doswell III, “The Future of Weather Forecasting: The Eras of Revolution and Reconstruction.”
45
forecast.” To this end, they recommended that, because such events are more rare and thus
free up time in the office, they might consolidate all NWS offices, reducing them to a few
regional hubs occupied with “Top Gun” forecasters, highly trained and skilled. This caliber
of expert, they argue, needs to be part of social science studies in order to understand what
characteristics qualified one as a “good forecaster.”140 Based on these findings, then,
forecasters would undergo special training and an exceptional education, as well as the
best tools and equipment at their disposal. Importantly, in this view, the job of tailoring
daily forecasts for specific user needs would be left to the private sector—those who
charge for their services. Forecasters, then, are experts in warnings. The computers and
the private sector constitute a new species of man-‐machine mix, one that services users
and preserves the scientific authority for public forecasters in the NWS.
The other option within this second view of forecaster identity keeps the services of
knowing user needs within the purview of the public forecaster. In an influential article
published by Allan Murphy in 1993, he asked what constituted a good forecast. Rather than
simply focus on accuracy and timeliness as the main qualities of “good,” he parsed
goodness into three types. A good forecast marries the forecaster’s judgment with the
prediction; it demonstrates coherence between the forecast and the observed weather; and
/ or it benefits a user, either economically or personally.141 The latter, Murphy argued, is
the good forecast that gets forgotten in the evaluation of quality and yet it is the most
important, constituting what he called a “requisite forecast.” He wrote that it should
“contain all of the information that potential users require to act optimally in the context of
140 Ibid. 141 Murphy, “What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting.”
46
their respective decision-‐making problems.142 In fact, it is this goodness of forecast that
alone constitutes the union of all three types of good. It is an ideal. In this instance, value is
not about the pure economics of accuracy or timeliness but the usability and helpfulness of
the forecast. As Murphy notes, “forecasts have no intrinsic value. They acquire value
through their ability to influence the decisions made by users of the forecasts.”143 A
forecast focused on accuracy or timelines alone has no value; it must contain concern for
the user to be “good”—that is, it must contain a multiplicity of ethics, not just that of
accuracy.
This view accords with the one Snellman first proposed—before meteorological
cancer and the professions’ fixation on competition with the machines or with ever
increasing accuracy as the goal of the final forecast. A man-‐machine mix ought to include
both a commitment to accuracy and a concern for the users and their needs. Further, a
forecaster—a good forecaster—shows an interest in and skill for both. And like other
variations in the meaning of the man-‐machine mix, this one continues as a potential
identity for forecasters today. Yet, as my article demonstrates, threats of meteorological
cancer overdetermined possible forecaster identities, ones that may not entirely reflect
forecaster practices and potentialities.
Fears over automation also contributed to a limited view of the forecaster. A writer
in the 1960s proposed that what people object to in automation is not so much the loss of
labor but the change in the image of themselves. “There is a strong revulsion in many
people against admitting the possibility of machines behaving like human beings; they feel
142 Ibid., 282. 143 Ibid., 286.
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that this would be equal to admitting that ‘man is a machine.’”144 In one version of the
future, forecasters could see themselves as machines, whether as an extension of the
machine managing them from “over the loop” or as a representative knowledge base taken
separated from their bodies and mechanically integrated with the machines. It is an image
they have struggled against for nearly forty years in their publications and practices. As
more than one forecaster has revealed to me, even today, many refuse to “populate the
[forecast] grids” with computer model ensembles, preferring instead to work out the
forecast for every hour and every day for which they are responsible on shift. “They’re still
trying to make it their forecast,” one NWS manager noted.
Concerns over automation coupled with those over meteorological cancer
projected—and continue to project—a dominant image of the future in which forecasters
with all their complexity and expertise become irrelevant. What accounts for the staying
power of such anxieties? In part, the sociotechnical practices and developments of
forecasting have continued to shape and be shaped by the discourses of the man-‐machine
mix. For as much as this language tells forecasters what they ought to be, so, too, does it
point to what their technologies ought to be, as well.
A related way to say this is that there is also the power of the words themselves.
Evelyn Fox Keller and Elisabeth Anne Lloyd argue that, “Words, even technical terms, have
insidious ways of traversing the boundaries of particular theories, of historical periods, and
of disciplines—in the process contaminating the very notion of a pure culture. They serve
as conduits for unacknowledged, unbidden, and often unwelcome traffic between
144 Gabor, “Inventing the Future,” 142.
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worlds.”145 The worlds of automation, disease, and weather prediction illustrate the
movement and mutual shaping of these worlds with the man-‐machine mix as a point of
contact for all three. The language and the work together imbricate the knowledge
production of meteorology and the identity of the forecaster. Keller and Lloyd argue that
such word, “ they have force and leave traces:
Upon examination, their multiple shadows and memories can be seen to perform real conceptual work, in science as in ordinary language. They help to keep worldviews together, to bridge disparate (even contradictory) concepts, to insulate us from problems we cannot solve.146
The problem to solve here is what a forecaster ought to be and how to find possible ways
forward that reflect the best of who they already are.
Perhaps there is something to be gained here from the literatures that mine the
complexity of automation. One outcome of this self-‐directed activity, some have argued, is
that it freed many workers from the “burdens of production,” forcing them to reconsider
their place in the world. As Supreme Court Justice Arthur Goldberg asked in 1962 about
where automation leaves us as laborers: “But are we, as a people, prepared to turn the
leisure time we gain to constructive use, to recreation in its true meaning, the “re-‐creation”
of our lives?”147 What does it take to re-‐birth a profession, to transform problematic
identities of a group of people to something more in line with the multiplicity of their
obligations? Recent efforts in the National Weather Service to “evolve” their underlying
philosophy from one of accuracy to one of ‘deep relationships’ with others may offer a
beginning. Anxiety is high as forecasters still grapple with notions of automation and
145 Keller and Lloyd, “Introduction.” 146 Ibid., 2. 147 Goldberg, “The Challenge of ‘Industrial Revolution II,’” 7.
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meteorological cancer amid new paradigms for their warning practices.148 The man-‐
machine mix is central to these discussions.
There might also be hope in the metaphor of meteorological cancer. It could be re-‐
visited as an indication of new kind of forecasting profession. Cancer, as is commonly
understood, is an abnormal growth of cells. The cancer introduces an imbalance into the
body, with cells multiplying in ways characterized as out of control. These then spread, or
metastasize and colonize other cells. But the image of cancer also challenges us to re-‐see
boundaries of normal and disease, of life and death. And in this work, cancer becomes a
sociotechnial process. As Lochlain Jain’s work eloquently notes, cancer is a noun but it is
also a verb, “an adjective, a shout-‐out, indeed a grammar all its own.”149 Perhaps in the
diseased form of practice that had polluted Snellman’s vision of a true forecast, there is
likewise room to “speak to—and from within—the [cancerous] complex” of automation.
Multiplicities of Accuracy in Weather Forecasting
Various permutations of the man-‐machine mix have given rise to visions of what the
forecaster as scientist ought to be just as it has generated a multiplicity of accuracies that
manifest today in national debates. Whether meteorologists predict daily sun and rain,
winter storms, tornadoes and flash floods, people in communities affected by such weather
express various expectations for how correct predictions will be.150 Some dismiss
predictions based on their own personal experience with forecasts that turn out to be
148 Doswell, “Weather Forecasting by Humans-‐-‐Heuristics and Decision Making”; Fine, Authors of the Storm; Pagno et al., “Automation and Human Expertise in Operational River Forecasting.” 149 Lochlann, Malignant: How Cancer Becomes Us, 223. 150 Morss, Demuth, and Lazo, “Communicating Uncertainty in Weather Forecasts: A Survey of the U.S. Public.”; Lazo, Morss, and Demuth, “300 Billion Served: Sources, Perceptions, Uses, and Values of Weather Forecasts.”
50
wrong to varying degrees. Others expect forecasts to be precise, with the prediction
matching the outcome. This is especially true when weather turns dangerous and threatens
lives, compelling forecasters to issue warnings, which explain the nature, significance,
timing, and spatial details of a threat. But just what does it mean to be “correct” and how
might this term differ between meteorologists and their publics? Evidence in news media
suggests the disparity is significant.
Accounts of storms that came “without warning” reverberate in media stories across
the country each year. In 2015, for example, a tornado touched down in a small Western
town, destroying multiple homes but sparing lives. At a town hall meeting the next day, the
community expressed frustration at a forecaster’s suggestion that they had received notice
of the advancing storm. “What warning?” many shouted. Those employed by the National
Weather Service (NWS), who in the United States are responsible for issuing free public
predictions, found themselves defending the information they had created and
disseminated. Many people revealed that seeing the tornado itself move through their
community functioned as their first indication of a threat, a challenge to claims of the
warning’s timeliness and precision. Forecasters would later learn that warnings issued that
day had failed to travel over mobile phones, one of the more common dissemination
methods for weather alerts, until hours after the storm passed.151 Still, NWS forecasters
met their agency’s criteria for internal success, accounted for through reports of the
tornado called in to their office as they issued the warning, and the appearance of the
tornado within the area bordered by the geometrical shape of their warning, the polygon.
So just how is the accuracy of a weather warning known in the NWS?
151 Based on my ethnographic work at a National Weather Service office.
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There are many possible answers, a few of which I classify below (See Table 1). First
is what I call performative accuracy, in which the warning functions as a “speech act,”152
which Butler argues, helps to “define the identity” of forecasters as protectors of their
publics. As illustrated in the above example, the warning is a public demonstration of the
forecasters’ expertise, as evidenced by the ability of the warning to match the spatial,
temporal, and categorical characteristics against the actual occurrence of the weather
phenomena for which a warning was issued. This performance is conducted through a
demonstration of accuracy, which serves to legitimate for their public the forecasters as
good scientists.153 The lay public in this small town expected such straightforward
accountings.
Another type of performative accuracy involves public trust in expertise. Research
across the social sciences suggests people assume that they will receive a warning over the
television, for example, and later personally experience the threat or hear about its
existence proximate to them on the news that evening.154 That is, their sense of accuracy
imbricates risks associated with warnings and their own local epistemologies and
experiences. If they do not have such evidence, these publics may not trust future warnings
and thus may not act on them later on.155 Many such findings about the lay public situate
152 Butler, Gender Trouble: Feminism and the Subversion of Identity. 153 Fine, Authors of the Storm, 174. 154 Mileti and Fitzpatrick, Communication of Public Risk; Mileti and O’Brien, “Warnings During Disaster: Normalizing Communicated Risk”; Brown, “Risk Communication Across Cultures: A Study of the Impact of Social Context, Warning Components, and Receiver Characteristics on the Protective Action of African Americans”; Donner, “An Integrated Model of Risk Perception and Protective Action: Public Response to Tornado Warnings”; Schumacher, “Multidisciplinary Analysis of an Unusual Tornado: Meteorology, Climatology, and the Communication and Interpretation of Warnings,” 2010. 155 Gruntfest et al., “False Alarms and Close Calls: A Conceptual Model of Warning Accuracy”; Simmons and Sutter, “False Alarms, Tornado Warnings, and Tornado
52
accuracy, then, as a sociotechnical issue bound mainly by definitional and communication
challenges. What may also account for such expectations is what Szerszynski156 called the
“performative dimension” of trust, which functions in two related directions: “Expressions
of trust in institutions can be at once performances of a ‘trusting’ role that are thrust onto
dependent communities, and also directive declarations whose intention it is to remind
institutions of their obligations to live up to that trust…” That is, accuracy is a currency of
trust exchanged between a particular public and the institution responsible for their safety.
Table 1 Types of Accuracy in Weather Warnings
Type Definition Peformative Accuracy Accuracy that defines identity of forecasters as protectors
and builds trust with publics Administrative Accuracy Distillation of accuracy to statistical metrics that allow
bureaucratic accounting Disciplining Accuracy Measures of accuracy that have embedded expectations for
public behavior Expertise Accuracy Accuracy attributed to the expertise and skill of the scientist
Second is what I call bureaucratic or administrative accuracy, in which “methods of
measurement are […] highly rule bound or officially sanctioned.”157 Within the National
Weather Service, accuracy is measured mainly through statistics that can gauge forecaster
skill by comparing predictions or prediction errors (e.g., those that are wrong) against
Casualties”; Hoekstra et al., “A Preliminary Look at the Social Perspective of Warn-‐on-‐Forecast: Preferred Tornado Warning Lead Time and the General Public’s Perceptions of Weather Risks”; Ripberger et al., “False Alarms and Missed Events: The Impact and Origins of Perceived Inaccuracy in Tornado Warning Systems”; Morss et al., “Flash Flood Risks and Warning Decisions: A Mental Models Study of Forecasters, Public Officials, and Media Broadcasters in Boulder, Colorado,” 2015; Morss et al., “How Do People Perceive, Understand, and Anticipate Responding to Flash Flood Risks and Warnings? Results from a Public Survey in Boulder, Colorado, USA.” 156 Szerszyniski, “Risk and Trust: The Performative Dimension,” 250. 157 Porter, Trust in Numbers: The Pursuit of Objectivity in Science and Public Life, 5.
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climatology. In gauging the accuracy of forecasts in hazards that either occurred or did not
occur, such as a tornado, three equations are used: Probabilities of Detection, or “how well
events are covered by warnings;” Critical Success Indices, or the percentage of time a
forecast event matches an observed event; and False Alarm Rates, or the percentage of time
warnings failed to verify.158 These statistics primarily focus on the relationship between
the forecaster’s abilities and their governmental accounting infrastructures that come
together to facilitate the NWS as a “center of calculation.”159 Thus, while concerns over
public safety may be related to such measures in the judgments and actions of individual
forecasters, the experiences of communities affected by dangerous weather are
disappeared in these metrics. Numbers, as Nicholas Rose notes, turn “a qualitative world
into information and [render] it amenable to control,” allowing "a machinery of
government to operate."160 Accuracy, in this instance, is a mechanism of measurement that
ensures the agency is accountable to its administrative and fiscal institutions.
A third kind of accuracy is disciplining accuracy, or an accuracy that embeds within
it an expectation for how the general public ought to behave when they receive a warning.
It is best exemplified by the concept of lead time, or the minutes of advance notice between
the issuance of a warning and the moment dangerous weather occurs.161 Rather than rely
explicitly on statistics, lead time necessitates that forecasters verify the appearance of a
weather event and compare it to the temporal and spatial extent of the warning box. In the
158 Office of Inspections and Program Evaluations, “NWS’s Verification System for Severe and Hazardous Weather Forecasting Needs Modernization”; Lasorsa, “Verification Statistics.” 159 Latour, Science in Action: How to Follow Scientists and Engineers through Society. 160 Rose, “Governing by Numbers: Figuring out Democracy,” 676–77. 161 Office of Inspections and Program Evaluations, “NWS’s Verification System for Severe and Hazardous Weather Forecasting Needs Modernization.”
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process of creating a warning, forecasters use a software program to draw a shape, which
they call a polygon, around the representation of the threat on a computer screen. This
gives them a concrete object with which to measure minutes from identification of threat
on radar to its appearance via reports from different publics. While indicative of forecaster
skill, this type of accuracy is also reflective of the relationship between forecasters and the
assumption about how people affected will behave. In the discourse of public warnings, for
example, forecasters advocate for changes to the system that might better encourage
people to take what many call “appropriate actions.”162 What is appropriate usually refers
to actions that lead people to stay safe but it also implies taking those actions specified by
experts, such as sheltering or evacuating. “Appropriate” also calls attention to a limited set
of choices that ignore the situatedness of people’s lives.
Finally, and most relevant to my argument, is what I call expertise accuracy, or a
correctness and precision that exemplify the abilities of an expert, in this case, a scientific
expert. Among the many elements of a field that scholars argue distinguish it as science or
non-‐science are those of objectivity, experimentation, verification of results, and predictive
accuracy.163 Meteorology is no exception. To count as good scientists, their discourse
suggests, forecasters who issue warnings should do so based on objective evidence (e.g.
detection technologies like Doppler radar) whenever possible and then be able to verify
their product164 in order to demonstrate accuracy, or in what might be called a “perfect
162 Committee on the Assessment of the National Weather Service’s and Committee on the Assessment of the National Weather Service’s, “Weather Services for the Nation: Becoming Second to None,” 52. 163 Popper, “Conjectures and Refutations.” 164 Barnes et al., “False Alarms and Close Calls: A Conceptual Model of Warning Accuracy”; Kalnay and Dalcher, “Forecasting Forecast Skill”; Palmer and Tibaldi, “On the Prediction of Forecast Skill”; “Skill.”
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warning.”165 Accuracy, then, is a necessary condition of forecasting science. Yet, similar to
scholarship about the notion of skill,166 where such accuracy resides is a different question.
While instruments and tools assist the scientist in carrying out rigorous methodologies that
might lead to results deemed valid and impartial, or to theories they might test and verify,
scientists typically see the human as the driver and generator of knowledge production.
Their mechanical devices—radar arrays, computers, workstations, and thermometers, for
example—function as mere technological assistants in this enterprise. In the pursuit of
accurate predictions, forecasters imagine themselves as the expert whose decisions and
actions inscribe accuracy into the skill of their forecasts. They see accuracy as a function of
themselves and their scientific training. Yet they are, in their practices, more akin to
cyborgs in their “leaky distinctions” between human and machine.167
Conclusion: Return to the Present
In June 2016, I interviewed a director of the Norman test bed where new warning
technologies are being developed so that, some day, they can be automated. He pointed out
that not much has changed in terms of the man-‐machine mix or the anxieties forecasters
feel as they come into the test bed and participate in possible developments of their
profession. I asked him about the likelihood that such technologies might eliminate the
human from forecasting. He responded with skepticism:
165 Barnes et al., “False Alarms and Close Calls: A Conceptual Model of Warning Accuracy,” 1144. 166 Collins, de Vries, and Bijker, “Ways of Going On: An Analysis of Skill Applied to Medical Practice”; Collins, “The TEA Set: Tacit Knowledge and Scientific Networks”; Polanyi, “Tacit Knowing.” 167 Haraway, “A Manifesto for Cyborgs: Science, Technology, and Socialist Feminism in the 1980s.”
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Number one, algorithms aren’t sufficiently advanced that they can do it all. And number two, I think that the fact is that humans who are going to be making decisions at the end of all this [work], they’ll want to know that a human has been a part of the [forecasting] process. I just don’t think [users] react the same way if it’s a completely automated process.
The old story of automation meets a newer story of user expectation. As with cutting
edge technologies developed in the late 1970s, uncertainties about their function in the
context of automation continue to provide a reassurance that forecasters will remain “in
the loop.” But there is also the uncertainty of the user to contend with. Rather than
imagining some forecast factory in which the whir of computers can be heard echoing
hollowly in a room empty of humans as they generate predictive information, users want to
know their decisions are based on information alive with the pulse of human judgment.168
The director continued, optimistic as Snellman about the future of the man-‐machine
mix and the profession: “I think when you balance all that together, there is an optimum
mix between the human being and the machine…. and there’s going to have to be a lot of
discussions before we find [it.].” I wondered to myself if in these discussions any one ever
suggested that there might be other metaphors more rich and reflective of the diverse roles
I’d watched forecasters enact in their practices. The mix, I wanted to say, is an illusive ideal,
one that continues to retreat into the future with each successive generation. It resists the
resolution that forecasters have sought either in clearly demarcating between human and
machine. The separation, the director reminded me, is still as separate as ever. “There’ll be
plenty for the humans to do on that side of the equation,” he said, “and to continue to do for
a number of years to come.”
168 Northwestern Mutual, “People Plus Technology.”
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In this article I have traced a brief genealogy of the man-‐machine mix and the ways
it has shaped forecasters’ discourse about their identity as scientific experts and their role
in society. As an ethic that governs the weather prediction discourse, accuracy produces an
image of forecasters as part of an equation that sets them as both collaborators and
competitors with the machines that facilitate their practices. Threats to the forecaster
identity come from within and without, from the choices forecasters make about how to
engage computer model information to the agency’s continual search for ways to automate
the forecasting process in the name of efficiency. Challenges to traditional forecaster roles,
then, have reaffirmed the value of accuracy at the expense of others, such as focus on user
needs—something Snellman and others believe should be central to forecaster identity. As
others in the community have shown, the man-‐machine mix is only one alternative.
What I offer through this analysis is a broader vision of possible forecaster identities
reflective of multiple ethics in which the “man-‐machine mix” is less prominent and thus
less anxiety producing. As Helen Longino, philosopher of science, argues, “We should stop
asking whether social values play a role in science and ask instead which values and whose
values play a role and why.”169 To this end, I ask if an ethic of accuracy is the value that
ought to play the most dominant role in forecasting science or whether others that exist in
forecasting practice but are largely hidden from public view might join accuracy as part of
their professional identity of the science. Thus I open up the possibility for alternative
images that exist alongside those of the man-‐machine mix. Especially important are those
values, like care and concern, that better mirror what sociologist Phaedra Daipha suggests
is their dual “commitment to science and commitment to public service” which form “the
169 Longino, “How Values Can Be Good for Science,” 127.
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basic building blocks at the core of NWS forecasting identity and logic of practice”[italics
added].170 Accuracy of prediction and protection from harm are intimately entangled. If
scaled up and made visible,171 the multiplicity of ethics that attend to their role as public
servants might better align their practices with their mission to protect people.172
The multiple valences of accuracy produce a social imaginary for both forecasters
and their respective publics that, as STS scholar Shelia Jasanoff has said of such co-‐
production, “encode not only visions of what is attainable through science and technology,
but also of how life ought, or ought not, to be lived.”173 Figuring the man-‐machine mix, then
produces an open question for the kind of society forecasters want to create. I hope I have
shown that forecasters have a choice and that the alternatives already exist in their
histories, their genealogies, and their commitments.
Acknowledgements: This work was conducted with the help of a Graduate Student Visiting
Scholar Fellowship from the National Center for Atmospheric Research and a grant from
NOAA.
170 Daipha, Masters of Uncertainty: Weather Forecasters and the Quest for Ground Truth, 49. 171 Downey, “What Is Engineering Studies For? Dominant Practices and Scalable Scholarship,” 2009. 172 National Weather Service, “NWS Strategic Planning and Policy.” 173 Jasanoff, “Future Imperfect: Science, Technology, and the Imaginations of Modernity,” 6.
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Article 2: Matters of Concern Prologue
In August 2004, I graduated from Goucher College with an M.F.A. in creative
nonfiction, a genre that broadly sits under the umbrella of creative writing, alongside
poetry and fiction. In English and creative writing departments at various universities, this
degree used to be the studio equivalent of a Ph.D., a credential designed to reflect a
student’s understanding of the craft of “making” creative works that, in theory, others
might someday study and enjoy as literature. This framing exists in many creative
departments, with art historians studying the works of artists, music theorists evaluating
those of musicians, and so on. Since the early 2000s, however, English programs across the
country have begun to offer a Ph.D. in creative writing, requiring students to complete both
a creative component and critical component of their work. As one colleague who
completed a degree in this kind of program said to me, “Doing the work is no longer
enough—now they want us to justify what we do, as well.”
Traditionally, justification of creative writing comes from situating one’s own work
in a broader context and theory and from external evaluation, though a trade book
publication. Like all academic units, creative writing programs encourage their students to
seek out respected publishers through representation by an editor. This model, which has
been challenged by the recent world of digital and self-‐publishing, required students to
develop a literary voice and style unique to the genre. Students’ talent—and endorsements
from successful mentors—helped them to get book contracts, the ultimate measure of
success for a writer. Literary journals housed mainly at university presses likewise offered
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a pathway through the writing life, and if they were sufficiently reputable—with high
rejection rates and a history of publishing luminaries in the genre—then journal
publications might likewise contribute to academic success. When writing creative
nonfiction in a field such as Science and Technology Studies, there are additional
justifications but also benefits to be gained from engaging with the form. Thus, I examine
how creative nonfiction as a genre might contribute to goals of Science and Technology
Studies (STS) scholarship.
An interdisciplinary field such as STS is difficult to distill into a brief summary of its
aims and purpose without losing the complexity and diversity of scholars within it. Still,
from my perspective, STS work can be thought as asking two main questions (with related
sub-‐questions) in the context of the sociotechnical and technoscientific:
1. What counts, why and who decides? To what ends?
2. How and in what ways is the topic of inquiry more complex than commonly
thought?
In terms of scholarship, an important element in qualifying these questions as STS is the
identity and motives of the person doing the asking and analysis. So the two questions
proposed here exemplify STS if a third criterion is met: The author is likewise a self-‐
identified STS scholar and intellectually validated. (Not that others can’t do STS
scholarship. I’m talking here about how to know if something is STS scholarship from
within the confines of the field per a dissertation). Let’s apply these three tenets to the
following article:
1. What counts, why and who decides? Creative nonfiction can be justified as STS
scholarship, I argue, and should for two reasons. First, the genre is already recognized in
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university circles as scholarship for disciplines that often interact with and have faculty
affiliations with STS departments. Jim Collier and Bernice Houseman are examples, as are
Kristen Koopman and myself. Creative writing, science fiction, and the like are not always
STS but when the authors are trained in both, then their work should “count” as STS.
Another reason for creative nonfiction to count is that related hybrid genres already exist
in STS literature. Consider Bruno Latour’s Aramis or the Love of Technology. In this book,
Latour draws on fictional techniques to invent characters who tell the story from their
point of view, including that of the technology itself. He aims to create a “scientification” of
the story of Aramis, the personal rapid transit system in France. He invokes “a hybrid
genre…for a hybrid task,” he says, which is to highlight the tenets of his Actor Network
Theory but also to playfully explore the historical context of a failed technology.174
Similarly, his earliest work Laboratory Life includes a controversial label of fiction in the
acknowledgements. Other contenders include Donna Haraway’s “Cyborg Manifesto” and
Primate Visions, Rachel Carson’s Silent Spring, and dozens of others by writers, such as
Richard Rhodes, Oliver Morton, Diane Ackerman, and Naomi Oreskes. While not necessarily
labeled within STS as nonfiction, these books cross genres and are read in cultural criticism
classes in English and creative writing programs. Theoretical frames shared by STS and
creative writing—feminism, for example, or deconstruction—create a common genealogy
that ought to extend to publications.
To what ends? Like many in STS who wish to challenge linear models of knowledge
production, creative nonfiction also offers this type of intervention. In STS, this work has
174 Latour, Aramis, or the Love of Technology, 2.
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been called by many names: Mode 2 and action research,175 high church and low church,176
activist or militant research,177 and, more recently, making and doing.178 Thus creative
nonfiction may be seen as extension of an activist agenda in how it allow the scholar to
mobilize her interventions beyond traditional academic journals toward larger audiences.
Creative nonfiction, then, might also contribute to STS approaches to “action-‐
oriented” research. By “finding frictions” in a particular site and then describing them in
such a way so as to reveal possible interventions or to offer interventions through the
description, the scholar can “problematize distinctions between description and action.”179
Publishing an essay about the practices of forecasting that are often invisible in a
journalistic publication, for example, both reveals that hidden element of the forecasting
identity and potentially alters how forecasters think about themselves or how the public
views the value of forecasters as public servants. In this case, the description and
arrangement of the narrative performs the activism.
Creative nonfiction also functions, at times, as a form of science communication and
at others as a source of public participation in science and technology controversies. It can
transform the dense and difficult jargon of academia into narratives that illuminate the full
spectrum of actors engaged in a particular project—it shows the messiness and context, the
175 Nowotny, Scott, and Gibbons, “‘Mode 2’ Revisited: The New Production of Knowledge.” 176 Fuller, “Constructing the High Church-‐Low Church Distinction in STS Textbooks.” 177 Russell, “Beyond Activism/Academia: Militant Research and the Radical Climate and Climate Justice Movement”; Woodhouse et al., “Science Studies and Activism: Possibilities and Problems for Reconstructivist Agendas.” 178 Downey and Zuiderent-‐Jerak, “Making and Doing: Engagement and Reflexive Learning in STS.” 179 Zuiderent-‐Jerak, “Editorial Introduction: Unpacking ‘Intervention’ in Science and Technology Studies,” 231.
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motivations and interests of multiple sides. In this sense, creative nonfiction is a form of
knowledge production that can help issues of interest to STS scholars travel further.
2. How and in what ways is the topic of inquiry more complex than commonly
thought? Although done through different means, good creative nonfiction likewise offers a
“more complex than that” view of the world. Through the authors’ choices of subject
matter, the connections they bring together, the assemblages they create, nonfiction offers
writers an open genre flexible enough to account for the complexities they find. Like
Lochlain Jain’s work Malignant, a personal and theoretical nonfiction account of cancer,
writing that crosses nonfiction and STS needs a trustworthy persona to explore the rich
imbrications of self and world, self and others, self and self. In nonfiction, a candidate for an
STS-‐like work would be Siddhartha Mukherjee’s Pulitzer Prize winning book The Emperor
of All Maladies: A Biography of Cancer. Written by a physician who is fluent in science
writing, the book is represented by a trade publisher, Scribner, and is taught in creative
nonfiction programs across the country. Yet the book also asks us to think about what
counts as cancer and reveals a genealogical complexity of its subject in ways that an STS
scholar might. Not that the two—STS and nonfiction—are the same, of course, but there is
much overlap.
3. Identity and motivations. Finally, the distinguishing characteristic between
nonfiction and STS seems to involve to the author’s motivations, purpose, and training, and
related to this, the use of jargon. I come from both worlds, with an M.F.A. in creative
nonfiction and now a Ph.D. in STS. They inform one another in my work, though I have
rather self-‐consciously suppressed my literary roots for the past five years in courses and
writing. As someone who hopes to reach an audience larger than my committee or other
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Disasters STS scholars, however, I’ve recommitted to including in my dissertation work
that arises from my two trainings but is more aligned in its purpose with my audience who
are not necessarily STS scholars. In this regard, I am performing the work of “making and
doing”180 or in the parlance of nonfiction, “good writing.”
Much as ethnography recreates a field site, creative nonfiction recreates the context
and content of the world it illuminates. And it relies on the techniques of fiction to do so—
by recreating scenes, using description, developing characters, using dialogue, and offering
internal reflection. Creative nonfiction writing propels the reader through the text as
though one is “there” with the author, though information about what the reader is meant
to learn is not always explicit. In the case of the article that follows, the narrative itself
relies on juxtaposition, dialogue, selection of detail, and personal reflection to build an
experience of the reading as a documentary film might. The choices of which scenes to
include and when, of which characters appear and how they are described, and of how to
pace the action—these function as devices that create meaning in the spaces and overlaps
between them.
Next, I offer a brief overview of nonfiction types so the reader may understand what
creative nonfiction is and how it functions. In general, there are three genealogical lineages
for nonfiction. In no particular order, they are journalism, autobiography, and essay. In the
1960s, a group of writers created a movement they called “New Journalism” to describe a
novel approach of writing that married the techniques and tenacity of journalism with the
stylistic approaches of fiction. Writers like Tom Wolfe, Truman Capote, Hunter S.
Thompson, Norman Mailer and Gay Talese are examples of early practitioners of this 180 Downey and Zuiderent-‐Jerak, “Making and Doing: Engagement and Reflexive Learning in STS.”
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writing form. Today, one might hear other iterations of this genre as “literary nonfiction” or
“narrative journalism” or “long form nonfiction.” Writers who practice in this field hold
verity—or accuracy of retelling—as the highest standard. Writers in this sub-‐genre, then,
spend time researching their narratives as a reporter might, finding multiple accounts that
validate “the truth” of a particular event. To distinguish themselves from journalists, they
tell the story “slant”—that is from a unique point of view and with a unique voice
sometimes through the techniques of fiction, even if not fictionalizing. For example, in 1965
Gay Talese tried to set up an interview with Frank Sinatra for an article commissioned by
Esquire. When the singer turned him down due to illness, Talese instead interviewed
people around Sinatra and observed him as he was able. The resulting work “Frank Sinatra
Has a Cold” is now a seminal work in New Journalism, both in technique and approach.
New Journalism centers the author’s voice in the narrative. One “hears” the author’s
voice above all others—she make sense of the story, guide the reader through facts,
anecdotes, descriptive language. As with other genres in creative writing, the authorial
voice and style, is the most important aspect of successful writing. Students can spend
years “finding their voice,” honing it through various publications, looking for authenticity,
uniqueness, even resonance. The writer’s ability to tell a good story, we’re taught, hinges on
this elusive and hard-‐won attribute of our writing. The reader experiences the narrative
through the writers, the way the author arranged the characters, the setting, the details
revealed or omitted. So writers must work hard to “hook” the readers, to keep their
interest, to show them something they can’t see otherwise. “Show, don’t tell” is a hallmark
truism in nonfiction. You must recreate what you see and experience. The writer, then, is
the instrument of the “well told.”
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The much older genealogical thread of nonfiction, autobiography, relies not only on
the author’s voice for success but the dramatic and unique nature of the life being re-‐told.
According to British poet Robert Southey, traditionally people wrote autobiographies in
middle and older age when they had attained a certain distance and reflection on their
lives. Many autobiographies gained success because of the author’s fame, though fame was
not always a self-‐attribution and not all books were published during the lifetime of the
author. Ben Franklin’s family published his autobiography after his death, for example.
Other books detailed important sociocultural points of view: Harriet Jacobs published her
account of life as a slave girl under a pseudonym since it exposed the treatment of slaves in
the South; Vera Brittain’s account of the lost generation after World War I is part tribute,
part reflection from an early feminist perspective. Autobiography, then, is a “long view” of a
life, one situated in the partial truth of history.
A related genre, memoir, highlights a shorter episode of one’s life. It shifts readers’
focus to incidents the author experienced that might resonate with (or shock or horrify)
readers. In nonfiction circles, the 1990s are the decade of “the confessional memoir,” an
explosion of books written by those who had been subject to rape, alcoholism, cancer,
abuse, and other difficult experiences. Criticized for their shock value and over-‐sharing,
authors in this genre care less about “verity” since personal memory and time erode
“truth,” they argue. Instead, their work is more voyeuristic, answering questions such as,
What are the limits of what can be shown? In Kathryn Harrison’s memoir The Kiss, for
example, the author reveals in great detail the affair she had with her estranged father after
they were reunited in her adulthood.
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Still, some sense of verifiable truth is important. Should an author be caught lying
public judgment is swift and merciless, as some have discovered. James Frey invented
people who later died in his book; Herman Rosenblat invented an entire experience in the
concentration camps; and Margaret Jones lied about her identity—these writers’ lies led to
public controversy and private shame. They’ve had contracts voided, books recalled, and
reputations ruined.
Memoir, thus, combines the concerns of authorial voice with a talent for either
telling a dramatic story such that the reader feels they are living it or telling a story that is
seen as having lessons one can learn. Because of the popularity of the subject and the
telling, however, memoir is less “academic” than something written from a journalistic
point of view. It may bring financial and popular success to the author but can also call into
question the author’s intent, motivation, personal life, politics, etc. There are exceptions, of
course, but some programs steer students away from memoir and toward the personal
essay should they have an interest in investigating their own interiority.
The essay is an amorphous genre with roots in the kinds of self-‐reflective writing of
people like Michel de Montaigne, known for popularizing the form in the 16th Century.
Styles vary from more formal critique, literary analysis, and argumentation to less formal
varieties that explore the author’s ideas and experiences. The former, according to essayist
Phillip Lopate has a “seriousness of purpose, dignity, logical organization” more in keeping
with “factual or theoretical prose writing.” Different sub-‐genres that have specific foci and
might be considered more formal include nature writing, travel writing, and science
writing. However, the latter, Lopate writes, engages
the personal element (self-‐revelation, individual tastes and experiences, confidential manner), humor, graceful style, rambling structure, unconventionality of novelty of
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theme, freshness of form, freedom from stiffness and affectation, incomplete or tentative treatment of topic.181
Personal essay, then, is a form that can be characterized by fragmentation and nonlinearity
such that it creates meaning though juxtaposition of images, temporalities, and episodes.
Timelines may not be linear, transitions may constitute no more than a double space on the
page or in inclusion of this mark (***) as an indicator of narrative shift. In more
experimental forms, the essay may be combined with the conventions of poetry to create a
lyric essay, a form that requires more work on the part of the reader to make associations
and connections. Other experimental forms include “short short” or flash nonfiction, that is,
pieces completed in a paragraph or a few sentences, and others may play with point of
view, or second and third person perspectives.
A difficult genre to clarify, the essay is often defined by what it is not: it is not fiction
and it is not poetry. It is not autobiography nor memoir. What it is carries forward as
authors situate themselves as reliable narrators or not, is a sense of one who treats the
topic fairly. At its best, personal essay writing creates an emotional intimacy with the
reader not based so much on tidy conclusions or direct explanation but on musings,
imaginings, and a conversational style. Motivated by questions such as “What do I know?”
and “What will resonate?” Lopate suggests “the struggle for honesty is central to the ethos
of the personal essay.”182 Thus, the voice and the writer’s efforts to engage the topic matter
deeply. Finally, the form encourages play between the writer’s personal interests (insights,
anecdotes, and descriptions) and issues of concern in the world. For some, this creates a
tension between the personal and the more general, wherein a specific instance, memory, 181 Lopate, The Art of the Personal Essay: An Anthology from the Classical Era to the Present, xxiii–xiv. 182 Ibid., xxv.
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or event may trigger themes that move out beyond the self to capture issues of important
to a broader audience.
These forms of nonfiction vary dramatically but have a common aim of being read
by and affecting a large “general” audience. In part, this aim is imbricated with the trade
publishing world, which awards contract amounts based on the number of copies an editor
believes a book will sell. Thus, writers aim for a large base of readers—not everyone, but
large enough to merit, say, a six-‐figure advance. But it’s not just about money. There is also
a desire among most nonfiction writers I know to share what they have experienced or
have learned with others. They are caught by the topic of their book idea—a book about
extinct birds and the value of contemplating extinction to society, for example. They can
write as public intellectuals, transforming their research into provocative polemics on race
and gender, for example. They ask questions, teach others, and critically intervene183 in the
worlds they reveal. While this is an oversimplified and abbreviated account of the
nonfiction form as it was taught to me, I hope I’ve given a sense of the variations of
scholarship in creative nonfiction.
My goal in the essay that follows is to reveal to forecasters how the ethic of care
already resides in and is mutually reflected in technical and nontechnical dimensions of
their work, but this is something I won’t say directly. The other articles in my dissertation
do this kind of analysis in ways appropriate to the audience. For my nonfiction work, I am
allowing the associations between experts and publics, or more precisely between
forecasters and a particular instantiation of a public, to generate meaning. Much as one
would hope to watch a film without continual narrator voice over, I have minimized my
183 Downey and Dumit, “Locating and Intervening.”
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authorial voice and am letting the scenes “speak.” STS, then, is evident in my choices of
material, the alternative image of care I help reflect, and the situatedness of this article
within my dissertation more broadly and my own education specifically.
A few notes about how to read this piece.
The title itself is meant to evoke a sense of the intent for this piece. I’m not explicit in
my use of the word care throughout because the scenes themselves were chosen to
demonstrate care. And I select care as an explicit focus of this work because care and
concern remain largely invisible in the public image of the forecaster—they are masked by
the dominant image of accuracy. However, I do come close to mentioning the title in the
introductory section by referencing “matters of love and concern,” a variation on what I
mean by care. The title and structure also invoke Latour’s article, “Has Critique Run Out of
Steam?” and his use of “matters of concern” to highlight a direction of critique as “not away
but toward the gathering, the Thing.”184 What I am hoping to offer in this bricolage is a
multiplication of meanings, an opening up of a conversation, perhaps even a new way of
seeing.
This is part of the essay’s work of showing and not telling. I begin the article “in
medias res,” or in the middle of things, a common and device to hook the reader. I then use
a narrative arc, or a gradual increase in action and tension to bring the reader to a specific
climactic point in the work. Such a moment is not meant necessarily to be more revealing
than the others, but it helps pace the writing, creating a sense of drama and engagement.
Throughout, I use the stylistic devices of double spaces between sections that are still
linked together to show a soft transition. I use three asterisks (***) to create hard 184 Latour, “Why Has Critique Run Out of Steam? From Matters of Fact to Matters of Concern,” 246.
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transitions that signal a potential shift in tone, subject matter, or time. Again, these create
an episodic character in the reading akin to film techniques such as soft takes to transition
between scenes and jump cuts where the transition is abrupt. They are common practice in
creative nonfiction publications.
To reinforce this notion of episodes, I am following another form called the
“braided” essay in which two or more narratives alternate in some pattern throughout the
work. In this case, I braid the story of Julie with a patchwork of forecaster experiences and
points of view. These are connected to the more reflective sections that introduce concepts
such as Tornado Alley (Oklahoma) and Dixie Alley (Alabama) to show the similarities of
experience across them. Pauses for reflection also offer the reader a break from the action,
and introduce reflective depth that builds alongside the action. Finally, the piece ends not
with a conclusion but at least a sense of resolution. That is, the denouement of the piece
offers a sense of closure to the article but not to the subject. In fact, like many STS works, a
creative nonfiction essay leaves the reader with more questions than answers but with a
sense of satisfaction in the treatment of the subject in the context of the genre and form.
Finally, a note about format. Most personal essays do not include formal in-‐text or
footnote citations. Instead, such information is explained in the appendix or index. Other
times, a simple author and book title in the text suffices. In this regard, the essay is more
aligned with its sister genres, fiction and poetry. Still, for the purpose of the dissertation,
I’ve included footnotes and a bibliography.
These are lofty aspirations, of course, and will be judged by the peer-‐reviewed
publications I’ve selected to submit this to. I’ve also asked my M.A. thesis advisor and
creative nonfiction writer Chris Cokinos to review this manuscript for suggested edits and
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publication venues. He’s published two books of nonfiction, two books of poetry, several
essays and critical theory articles, and is currently an associate professor of English at
University of Arizona where he is a mentor in science communication and the Carson
Scholars Program.
In the creative nonfiction publication process, many literary journals where such
work finds a home offer the author an option of “simultaneous submissions.” This allows
writers to increase their odds of publication for one work while giving the editors notice
that if they want to publish, they must make the decision quickly. Most literary journals
take between 3-‐6 months to make a decision and few ask the author to make edits to their
work before publication. Thus, there is rarely a revise and resubmit process. Acceptance for
publication, then, leads to a longer circulation.
Word limits for creative nonfiction work vary by journal, especially those that take a
variety of nonfiction work. I have written this article at a length that gives me the most
choices—between 5,000 to 7,000 words. Further, the journals I’ve identified are well
respected and are regularly ranked among the top 50 literary journals in the country. I
have published before in each of them, though not for a while. In 2004, for example, I was a
runner up in Fourth Genre for its Editor’s Prize and I have written for years with an editor
at The American Scholar, though in a different section of the journal. With this in mind, I’ve
selected the following journals to simultaneously submit to.
• River Teeth: no word count. From their site: “River Teeth is a biannual journal
combining the best of creative nonfiction, including narrative reportage, essays and
memoir, with critical essays that examine the emerging genre and that explore the
impact of nonfiction narrative on the lives of its writers, subjects, and readers.”
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• Fourth Genre: 8,000 words. From their site: “Given the genre’s flexibility and
expansiveness, we welcome a variety of works ranging from personal essays and
memoirs to literary journalism and personal criticism. The editors invite works that
are lyrical, self-‐interrogative, meditative, and reflective, as well as expository,
analytical, exploratory, or whimsical. In short, we encourage submissions across the
full spectrum of the genre.
• American Scholar: 6,000 words. From their site: “The American Scholar is a
quarterly magazine of essays, fiction, poetry, and articles covering public affairs,
literature, science, history, and culture. Published since 1932 for the general reader
by the Phi Beta Kappa Society, the Scholar considers nonfiction by known and
unknown writers, but unsolicited fiction, poetry, and book reviews are not accepted.
The magazine accepts fewer than two percent of all unsolicited manuscripts.”
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Matters of Concern [Word count: 6375]
Julie stood in the doorway of the hospital, waiting for the young couple in a car to
open the door.185
She looked up at the sky, noting the darkening horizon to the southwest. What she
couldn’t see just then was the mass of wind and debris heading her direction at 40 miles an
hour. Because of its size, the tornado would have been difficult for any one of the 13,000
people in its path to distinguish clearly.186 Social media posts made by the National
Weather Service in Norman warned, “The tornado is so large you may not realize it’s a
tornado.”187 At over a mile wide, it might have seemed like a murky cloud taking up much
of the horizon, as it does in photographs from that day. Its eerie sound, described by some
as the whirring of jet engines, may have been the only signal of its existence.
“They just wouldn’t get out of the car,” Julie explained. She looked down at her
hands for a moment, as though she were contemplating the simple act of opening a car
door. She sat on the other side of a small table, her white blouse blending into the white
walls of the room, her dark blue skirt tucked around her legs. She looked small but the
raspy edge to her voice made me think she was much tougher than she looked.
“What did you do?” Laura asked.
Julie seemed to be looking through us. She shook her head and sighed.
“It's like they were too scared to open the door. Like they were frozen. I'd go to the
window and yell at them, ‘Get inside!’” She looked up at us as she yelled the words again 185 All excerpts taken from Robberson, Norman Medical Center Interviews. 186 Spann et al., Violent Tornado in Moore, OK. 187 National Weather Service, “May 20, 2013: Newcastle, Moore Tornado.”
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and I tried to imagine myself as one of the people in that vehicle, too terrified to move. “But
they still wouldn't listen and so I had to go out and open the door and practically pull them
into the hospital.” Weather forecasters who heard her story after the storm called her
actions heroic, inventive, even miraculous. To Julie, however, she was doing what she could
do given the circumstances.
Laura and I had been sitting in the room for almost 30 minutes listening to this
woman with blond hair and green eyes recount the afternoon of May 20, 2013, a day when
a tornado had destroyed most of the hospital where she worked. Laura, then a social
scientist at Mississippi State, fidgeted in her chair as I, a graduate student at Virginia Tech
University, continued to take notes on my yellow-‐lined paper. We’d been sent to Oklahoma
to interview Julie as part of an assessment on behalf of the National Weather Service. Our
goal, we were told by agency administrators, was to piece together the actions that
different people took as their community faced yet another EF-‐5 tornado—their second of
the strongest possible such storms in just over ten years.
Other members of our group, mostly meteorologists, would spend the day with their
colleagues in the National Weather Service, listening to them detail the unfolding of this
“event”—their common shorthand for disaster. They’d ask about how the technologies of
warnings performed, what processes they’d used to relay the warning, how prepared they
felt their community had been. Laura and I would later take surveys we’d created to a
crowded downtown corner and interview news reporters, emergency managers, and
members of the general public,. Our collective hope was that by assembling narratives from
different groups of people, different points of view and experiences, we’d have a more
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nuanced understanding of the tornado’s effect on this community and what problems we
might identify that the agency could address.
Although I had been sent to help write an account of what I would hear and see, I
didn’t anticipate how people’s stories would reconfigure my own expectations of what it
must be like not just to live with fear and loss in light of such horrible destruction but to do
so as a person with at least partial responsibility now for ensuring that others have the best
chance to survive. And I would come to see that what we first think of as problems often get
transformed through happenstance and surprise, challenged by perspective, and live
alongside what we call matters of love and concern.
Oklahoma sits at the center of what meteorologists label, Tornado Alley, a cluster of
states from Colorado to Missouri, South Dakota to Texas. But Tornado Alley has fluid
borders, on some maps encompassing more states than others.188 On one, the area of
highest risk shows up as an inverted boot, the toe sliding into Iowa and Minnesota. On
another, the alley is more like a blob of red in the center of the U.S. with the deepest red
indicating highest tornado count bleeding toward the edges of the Southeast and Midwest.
These images morph with the seasons, shifting the “alley” north or south, east or west. Like
other indicators of risk, the areas most prone to harm change depending on criteria experts
choose: number of dead, number of tornadoes, population demographics, and
characteristics of the tornadoes themselves. Select one combination and an area like Texas
188 Dixon, “Tornado Risk Analysis: Is Dixie Alley an Extension of Tornado Alley?”; Forbes, “What and Where Is Tornado Alley?”; Frates, “Demystifying Colloquial Tornado Alley: Deliniation of New Tornado Alleys in the Central and Eastern United States”; Gagan, Gerard, and Gordon, “A Historical and Statistical Comparison of ‘Tornado Alley’ to ‘Dixie Alley.’”
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seems to be the most tornado prone. Select another, and a state like Alabama suddenly
comes into relief.
Uncontested in this boundary work of tornado risk classification is Oklahoma. It sits
at the center of most maps, and within its borders, the town of Norman, a historical nexus
for atmospheric research, and adjacent to it a cluster of cities beleaguered time and again
by tornadoes: Moore, Oklahoma City, and El Reno. The landscape here undulates quietly
under the violence of collisions above, where cold air off the Rockies collides with warm
moist air off the Gulf and hot, dry air from Mexico. These elements come together in the
spring to trigger massive storms, called supercells, within which develop the vortex of wind
that extends from cloud base to ground, the storm the Choctaw who first settled this red
earth call “Mahli Chito,” tornado189
Other “Alleys” have emerged on maps over the past decade as populations expand
into larger urban centers and as more people witness tornadoes, capturing them in their
experiences and their technologies. The National Weather Service Storm Events Database,
an official accounting of storms, bears this out. It shows an increase in the number of
tornadoes over the past fifty years, an observation researchers suggest derives not from
more storms but an increase in the number of people who report them.190 Dixie Alley, in
the Southeast, for example, has arisen in the narrative of U.S. tornado history as a
competitor to Alley in the Plains. In number of deaths, nocturnal occurrence, population
vulnerability, tornado severity, and seasonal frequency—characteristics that
meteorologists use to measure risk—Dixie Alley trumps Tornado Alley. Its storms are
189 “Lesson of the Day.” 190 Verbout et al., “Evolution of the US Tornado Database: 1954-‐2003”; National Weather Service, “Storm Events Database.”
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different from those on the Plains.191 Whereas the most common type of storm in Tornado
Alley is the classic low-‐precipitation supercell (think “Wizard of OZ”) those in the
Southeastern U.S. mainly form as high precipitation tornadoes, or rain obscured winds that
make them difficult to see in the hilly terrain. And more deadly.
***
That afternoon, in Tornado Alley Julie knew she was in trouble. She’d seen the
“swirling green and red colors” that represented the radar signature of the storm on a
colleague’s cell phone and knew it would be bad. “I’d seen this form on TV several times so
I knew what it meant. It meant the tornado was coming at us.”
As an executive secretary to the safety administrator, Julie had some disaster
training. She’d been told the year before that she needed to participate in the scenarios. Her
boss, a tall, stout man in his mid-‐thirties, and director of safety administration for facilities
within the local medical system, told her, “You never know when you’ll be the only one in
the office when something happens.” She’d laughed, “I couldn’t even imagine that I’d need
all that training but I did. And thank God I had it.” On the day of the tornado, several safety
staff at the hospital had decided to head home to be with families. “Some went to go get
their kids and I didn’t blame them,” she said.
Julie knew she would be the one to make decisions based on that information. She
set about organizing people in the hospital, preparing them for the worst. She explained
that she’d begun to run out of room in the hospital for people and their pets to shelter.
Many were exposed between the interior wall and the glass windows on the exterior. She
knew this wasn’t safe but she’d already filled many of the smaller offices and patient rooms 191 Ashley, “Spatial Analysis of Tornado Fatalities in the United States: 1880-‐2005”; Ashley, Krmenec, and Schwantes, “Vulnerability due to Nocturnal Tornadoes.”
79
with people. “[W]hat could I do? I was running out of space. I mean, one woman was in
labor next to a pit bull. And there were a lot of people who didn't speak English. They were
frightened and had nowhere else to go so they came to the hospital. It was a safe place.”
Julie tried to think of other ways to configure the crowd so they’d survive, especially
those who were the most vulnerable so she decided to put them all in the cafeteria on the
first floor. It was the largest open area in the hospital and she knew she could fit most of
those who might need additional help, including many of the hospital staff. As an interior
room, the cafeteria had no windows and would be insulated by the hallway and exterior
wall. “In all I guess we had about 350 people taking shelter in the cafeteria and in the small
rooms on the first floor of the hospital,” she said. That’s a lot of souls to be responsible for
and she felt that pressure. “I prayed,” she said. She closed her eyes as she said this. “Hard.”
The room buzzed with the restless chatter of people uncertain of what they might
have to endure. They’d come to the hospital for procedures, treatments, convalescence, and
now they might die from an act of nature. Some people were crying and consoling each
other. Others stayed quiet, unsure of what to do.
Julie tried to calm herself so she could think. She had an idea.
“I stood up on a chair in the middle of the room. Now, I'm not very tall so I yelled to
everyone in the room, ‘Look at me! Notice what I'm wearing! Listen to the sound my voice!’
I said, ‘I'm going to tell you how we can stay safe and I need you all to listen.’” I could see
her in my mind, in her office attire, cupping her hands to her mouth as she yelled, the room
growing quiet. I could also imagine those in the room who didn’t know Julie or the position
she represented at the hospital wondering who this woman was that they should put their
lives in her hands.
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She likely had little time to consider peoples’ concerns about her qualifications.
They couldn’t know that she’d survived one historic tornado by enacting advice she likely
heard televised and broadcast with every potential tornado. They couldn’t know about her
boss’s decision to include her in hazard training. They couldn’t know she’d save them.
Julie gave directions. To be sure she’d have enough help after the tornado struck.
She corralled the half dozen physicians on staff that day and gave them an order that
stunned them. She opened the doors to the cafeteria’s large metal, walk-‐in refrigerator.
“Get in,” she told them. “At first they protested. They wanted to stay out and help. But I told
them that I needed them for triage. That I needed them to stay safe. And so they finally let
me put them in. I locked the door.”
Next, she sent a security guard up to check on a woman who was in labor and
couldn’t be moved because of her epidural. Two nurses had volunteered to stay with the
patient on the second floor and Julie wanted to see that they were okay. Later the nurses
would detail how the winds had come up around them as they huddled under blankets
with their pregnant patient. They had moved her to the hallway to keep her from the
exterior windows but couldn’t find outlets to let them keep the monitors plugged in. They
moved her back into the room just before the tornado hit.
Finally, Julie directed all the nurses to help her. “We put all the moms with new
babies in the center of the cafeteria and we create a circle of nurses around each one. Then
we took all the people in wheelchairs and gently lay them out on the floor and cover them
with blankets and tables. Everyone else formed a circle. We held hands. Some of us cried.”
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The tornado would be at the hospital at any moment. Julie knew this because her
boss, Shane, the Director of Safety for this and other medical centers in the area, had just
texted her two simple words: “Code Black.”
“I didn't know what that meant. I had to turn over my badge and look at the color
code to decipher his message. Black meant imminent danger. I knew Shane was telling me
we were about to get hit. So I told everyone to join arms with their backs to the wall. There
was a lot of crying and a lot of praying.”
There was likely a lot of praying in the Norman forecast office, as well. Located 10
miles south of Moore, Norman is a hub for many meteorological activities. The University of
Oklahoma, home of a top ranking meteorology program, sits near its heart, along with the
National Weather Center, a 250,000 square foot building, which houses several key
government weather prediction centers and research labs, such as the National Severe
Storms Lab and the Hazardous Weather Testbed. Clustered together on the third floor, two
interlinked centers sit adjacent to one another separated by glass. The Storm Prediction
Center, a national unit in the National Oceanic and Atmospheric Administration, is
responsible for monitoring for threats from tornadoes, severe thunderstorms, wildfires,
and winder weather and issuing watches. To its left, is the local National Weather Service,
one of 122 across the country responsible for issuing warnings and advisories for severe
weather in their County Warning Area. This geopolitical area consists of 48 counties in
Oklahoma and 8 in West Texas and includes about 2.5 million people, who rely on the office
for daily forecasts and hazard warnings.192 On May 20, 2013, thousands of individuals like
192 Marsh, “Population of NWS WFOs.”
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Julie would have found themselves inscribed within the red box-‐like shapes that, on
television and mobile phones, constitute a tornado warning.
As the storms blistered across their area, forecasters in Norman sent out alerts. In
their office, the air filled with the buzz of voices, some emanating from the wall of television
screens at the front of the room, offering what forecasters call “situational awareness” of
local news coverage.193 Other voices came from those on the telephone with emergency
managers, government officials, first responders, and concerned citizens calling in for more
specific information. HAM radio operators communicated with their storm spotters in the
field, the equipment itself “talked” to the forecasters, warning them of deadlines for
forecast products or reciting the warning information conveyed over NOAA weather radio.
But the most haunting voices came from the forecasters talking to each other, and at times,
the tornadoes on the television screen.
According to field notes shared publicly by Jack Friedman, an anthropologist who
conducted observations in the NWS office that day, the forecasters felt a sense of dread and
exhaustion watching the wedge-‐shaped mass on the screen.194 Friedman had joined a panel
of meteorologists representing different points of view of that day, from those in the
National Weather Service, those who are researchers in that community, those who are
studying the warning system. All of these people live in these neighborhoods, have taken
shelter from tornadoes or lost parts of their homes to storms—roofs, fences, trees.
Collectively, they’d designed this presentation to help experts in the weather community in
attendance understand the lived experiences and timeline of events that unfolded in May.
193 NWS Norman Forecast Office / May 20, 2013. 194 Correia, Jr. et al., “Forecasting and Response to the 20 May 2013 Oklahoma City Area Tornado.”
83
As Friedman read excerpts of what he saw that day in the forecast office, the audience
quieted, the atmosphere of the room thickening with anticipation as he spoke. He began by
describing how the forecasters took calls from the public and storm spotters, reporting the
first appearance of the tornado that would hit Moore around 2:55 pm.
“Someone said, ‘This is going to be a big one,’ or ‘a significant one.’”
He read slowly, stoically, letting the images of that day take us to the office with him.
“Several people are on the phone getting the reports coming; others have run up and
are taking photos out the window or even pictures of the FO [forecast office]. The
tornadoes seem to be moving into Western Moore and look to be catastrophically big, as
somebody said, ‘really huge and really well formed.’ There’s a much more palpable panicky
feel because this is home for people.”
I’ve been in this office before and know some of the meteorologists well. They have
talked to me about what it’s like to be a forecaster in Norman. That day was the third in a
weeklong streak of tornadoes occurring nearly every day in Oklahoma. They had been
working overtime, not only issuing their regular forecasts, which continue through severe
weather, but the warnings, as well. They likewise were responsible for talking to a host of
decision makers and members of the public. And after each tornado, a subset of the office
heads out into the community to conduct damage surveys, walking slowly through the
debris, noting the characteristics of damage, the types and severity of destruction, which
they calculate in their software in order to come up an Enhanced Fujita Scale, or EF Scale,
rating for the storm. Often, they are some of the first on the scene, functioning as first
responders in their encounters with emotionally distraught victims who have lost
everything, including loved ones.
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The anthropologist slowed a bit as he described what happened next.
“Someone just said out loud, ‘Please weaken, please weaken.’ His leg is shaking, and
he’s saying, ‘Oh my god.’
“There’s another possible tornado around Lawton that people are trying to pay
attention to but it’s difficult when this [area] is everyone’s home.
“Someone: ‘It’s moving into Moore and southwest OKC.’”
“Someone: ‘It’s moving right over my aunt’s house.’”
“Someone else: ‘How many times do we have to deal with this?’”
‘Someone else: ‘This is going to be a long track, significant tornado through Moore.
There are no signs of occlusion or weakening.’”
Members of the audience gasped, stiffened as they tried to stifle emotions, though
some, like myself, quietly let the tears come. Those on the panel were no doubt reliving the
fear they had for their neighbors, their friends and families.
During the Q and A, one of the panelists noted he was constantly calling his family to
make sure they were in the shelter. That his wife had picked up their daughter from school
with enough time to get back home. Another told us how he said goodbye to his apartment
before heading to a shelter. “I’m sick of constantly saying goodbye to that place.” The
anthropologist, who joined them on the damage survey, mentioned the sound of the dying
horses, which were still being located and put to sleep. “I can’t even describe that scene,” he
said. The other panelists, who had been part of the survey, as well, nodded and lowered
their heads. I’m reminded of stories from the front during the First World War and the
descriptions of animals left on the field suffering during the night, unable to be rescued
85
because of continual bombing. I think of shell shock. Post-‐traumatic stress. The endurance
of tragedy and its bodily toll.
There were questions about the details of recovery, how they notified the public of
the threats, what they would do differently.
“I’m wondering if during the damage survey you’ve ever found a body,” one woman
asked.
The question caused many in the audience to shift in their seats. People whispered
about the appropriateness of the question, the directness of it in this context. But to my
mind, it was the perfect question, a symbol of the raw experience we faced in this room.
One of the panelists, a forecaster with the National Weather Service, looked down
for a minute. Then he said, still looking down. “I never have, thankfully. If I ever did, that
would be the end for me. My retirement.” He looked up at the crowd. “It’s not something I’d
recover from.”
***
Julie already had survived two tornadoes classified by winds over 200 miles per
hour. Her first occurred on May 3, 1999, when the now infamous EF-‐5 that struck the same
community of Moore. From that experience, she’d learned a great deal about how to read
the skies, the technologies, and the people around her.
She’d been doing home improvement projects with her boyfriend when she heard
the sirens blare. “I remember going to the bathroom,” she said, “which was the smallest
room in the house and it didn't have any windows. I had my little dog in my purse around
my neck. But we were renovating the bathroom and so there was no door. My husband
86
wedged our white couch into the doorframe just a few minutes before we heard the
tornado coming.” She continued: “You know, people talk about it sounding like a train or a
bunch of bees but me it sounded like metal on metal. It hit the house and I thought for sure
we would die. It seemed like the wind lasted several minutes but it was over quickly. And
you know that white couch didn't budge. It stayed put and kept all the debris from hitting
us.”
No doubt she drew on her emotions from that day, her understanding of the terror
of uncertainty—would they survive? And if they did, what would remain?
In recounting to Laura and me the minutes before the tornado hit, Julie paused, as
though the interview were over. She looked at us, through us, like weren’t in the room. We
knew she’d put the doctors in the refrigerator, sent nurses upstairs checking on a patient,
and had others encircled new mothers, their babies, and people in wheelchairs who had
been laid down on the ground.
Laura looked at me and then cleared her throat. “That must have been so terrifying,”
she said.
“I can’t imagine,” I added quickly, “what it would have been like.”
Julie turned to the side in her chair and looked down to her lap. She picked at
something on her skirt, then looked up again at us. “You two ladies seem like good
Christian women,” she said after the long pause. “So I’ll tell you what happened.”
I nodded, and I’m sure I blushed. I didn’t identify as Christian and wondered if I
should say something before she continued. Or perhaps she invoked a religious label to
suggest to us that she trusted us and how we would treat what she was about to say. Either
way, I felt the room suddenly close in a little as she began.
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“We were all in the cafeteria waiting for the tornado to hit when a huge wind rushed
to the building and blew the doors inward. It caught one of the security guards and she
rolled into the room through the doors. I’d sent her to do a quick check of the hallways to
make sure we had everybody.
“Then, there was this loud sound,” she said, “a bunch of things hitting the building,
some loud bangs.” She looked just above us as she spoke. “And then it was in the room with
us.”
She said the tornado appeared as a grey mass with things swirling in it, the sound
drowning out everything else. “After a few seconds I broke rank,” she said, “turned around
and faced the wind. I stretched out my right-‐hand at the tornado and started yelling, ‘In the
name of Jesus Christ I command you to leave! In the name of Jesus Christ I command you
leave!’”
The other nurses started cheering. “They yelled, ‘It's working Julie! It's working
Julie!’ And you know that tornado it lifted up and went over the hospital.”
“You know later the engineers explained it to me,” she said. She leaned forward
across the table a bit, as though she were finally sharing something confidential.
“They said the tornado had gathered all these cars in the parking and pushed up
against the wall the cafeteria. We were right behind this cafeteria doors. All those cars—
some three hundred of them—were piled into the doorway of the hospital and acted like a
leaver in diverting the tornado winds up and over hospital. I mean, I know that this is the
science behind why we're alive. But we all know that God saved us, that he listened when
we prayed.”
***
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Not all feelings of responsibility are easily shared, however, especially when people
die. “I have the most deaths on my watch,” one forecaster said aloud to the room. He kept
his back to me as he continued working on the computer in front of him.
“It’s true,” another said, glancing my way. “For whatever reason, when Tom works,
that’s when people die.” They both smiled weakly and went back to their work.
“It’s a curse,” Tom mumbled. His office had just been notified that someone had
drown during a flood of the local river when a few young adults had thought it a good idea
to try and tube the raging river as it rushed through the canyon. Tom had issued the flash
flood warning that morning and by afternoon, officials had found the man’s body.
I’m reminded of my first National Weather Association meeting in Birmingham,
Alabama, a society comprised mostly of operational forecasters. The state had just been
ravaged by some 350 tornadoes weeks before, the destruction still evident around us in
neighborhoods close by. During one of the session breaks, a forecaster at a local National
Weather Service stood in front of his colleagues with a microphone. “Look,” he said, “I know
many of you want to know how I’m doing—how my staff is doing. And I appreciate your
concern.” He took a deep breath, letting it out slowly to stifle the crack in his voice. “But I
just can’t. I can’t talk about what happened yet. I hope you’ll respect that.” He handed the
mike to a person standing nearby and wiped at his eyes.
Later that week, at the awards ceremony, a member of the community stood and
gave an invocation before the program began.
“Dear Lord,” he began. “Guide us in our predictions that our judgments may be clear
and our forecasts true.”
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All heads bowed, arms folded or placed carefully on the table. The prayer continued,
asking God to bless the community in their daily jobs as public servants. It asked that He
watch over those who had lost homes and loved ones in the recent storms and over the
forecasters still grappling with their own emotions. I could hear people sniffling as he
spoke.
Later, forecasters in Alabama were able to talk about what had happened. They put
together a series of videos to explain their experiences with the tornadoes that year.195 The
Meteorologist in Charge, Chris Darden, opened the video with a mix of explanation and
description of those days. “Let’s be clear,” he said, “we all are professionals and we all have
a job to do.” However, as the storms rolled through one community after another, he
admitted, “We were all visibly shaken.”
In one particular community, forecasters saw on their radar screens the shape of
something that represented a “debris ball” or the detritus of the storm being picked up by
their instruments. “That’s when we knew it was bad,” Chris forecaster said. Another noted
that whatever had been there before was gone.
The video overlaid scenes of destruction with the colorful splotches on radar.
Images shifted from their work to the damage on the ground, from them at their desks to
people standing among ruins. “Damage reports were slow to roll in from a few locations,”
Chris said in the voice over, “and we knew why. It wasn’t that this wasn’t a damaging
tornado, it was because there was no one left to report the damage. In our gut we knew
there were mass injuries and very likely a large number of fatalities.”
195 National Weather Service, NWS Huntsville: A Look Back on the April 27th Outbreak-‐-‐Part 1.
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The video cut to an image of the tornado from that day, a smudgy gray shape on the
horizon, taking up much of the television screen. “We can only pray that our warnings,
updates, and diligent work could prevent this from being a complete catastrophe,” Chris
said. He continued and explained that in the days following the tornadoes, forecasters
spent “many 16-‐hour days helping first responders” and “just being there for our
communities.”
I imagine the aftermath, pine trees broken in half, houses gutted, the smell of lumber
and the pungent remnant of gasoline from overturned cars thick in the air. People would
have been milling about the litter strewn in their yards or where there homes once stood,
wandering through wreckage to find that one photograph or toy, anything that might be
salvaged. Forecasters are often first to arrive in some of the more rural parts of the county,
the first to speak to those who have already begun to make sense of what had happened.
“The scenes of destruction out there were sobering,” Chris said, “even for a seasoned
meteorologist. As someone who has been on nearly 50 storm surveys, seeing entire
neighborhoods brought to the ground was horrendous….gut wrenching…almost too much
to bear.” Stories of survival, he offered give us “a little piece of joy in a large sea of despair.”
The phrase “on my watch” connotes personal obligation and accountability for
whatever happened while a person was in charge. It implies an all-‐seeing eye, a capacity to
observe the infrastructural and the intimate and then to control them. Prevent. Protect.
Intervene. This is perhaps where the phrase comes apart. The first person claims
responsibility for more than perhaps ought to be overseen. A shared sense of duty—our
watch—a collective effort to distribute the efforts of watchfulness seems more reasonable.
More ethical. Putting on one person the onus to account for things that ought to be viewed
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collectively elides opportunity for change as it masks moments of success and failure in the
larger apparatus of disasters.
A makeshift memorial had arisen amid the remains of a school in Moore where
seven children died, a response to the community’s grief. Seven small chairs had been
arranged in a semi-‐circle behind the chain link fence now surrounding the empty
foundation. Behind them, wooden crosses set in the ground marked the children’s deaths
and on the front of each, a placard with the child’s name. Within the links of the fence and
against its base, people from across the country had placed teddy bears, t-‐shirts, flowers,
flags, dolls, and other personal items. We all mourned, felt these deaths senseless,
unbearable, a tragedy without blame. Teachers had instructed children to crouch in the
windowless center of the school, in bathrooms and closets. They did all they could. The
warnings were issued, sirens sounded, actions taken, and under the darkened skies that
day, these seven had died.
***
The twister began as a wisp of gray wind swirling over a green line of fields near a
freeway in Oklahoma. Like a pencil on a map, the tornado quickly scrawled its mark across
the landscape for nearly twenty miles, gaining in speed to become at its widest, 1.3 miles in
diameter. Over the course of forty-‐seven minutes, the tornado moved through
communities, growing and ebbing in strength, killing two dozen people and injuring nearly
400 more.
News media outlets began broadcasting live coverage of the wedge-‐shaped wind as
it scoured the ground for miles. It struck a horse training area, Celestial Acres, where it
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tossed horses into power lines and impaled them with debris. Minutes later, the tornado
would destroy the first of two elementary schools, then the second, where seven children
would die as they lay trapped in debris, drowned by heavy rains after the storm passed. It
leveled house after house in its path, some structures torn apart, their foundations wiped
clean.196 Others were unrecognizable as buildings, the shredded wood and wire protruding
from debris like bones.
Around 3:15 pm, the tornado headed for the hospital, a glass and cement building
filled with over 350 people, many of whom were simply fleeing the storm. In a news article
written days after the disaster, one hospital administrator noted that their facilities had
experienced an increase in the number of people coming to them for protection. During the
storm, they had had “a basement full of folks who had nowhere else to go.”197 People began
arriving hours before the tornado, bringing whatever they could carry. At the forecast
office, meteorologists weighed the tornado on scales of technologies built of their collective
experiences and the expertise shaped by their technologies.
The Weather Channel played reruns showing wrecked buildings leveled across
several small towns and cities. Anchors on air yelled in horror as the rotating winds struck
the two elementary schools, local businesses, the town’s only movie theater. Aerial footage
revealed rows of homes shredded and mangled, cars upended in yards, trees stripped and
flattened. From high above, the tornado’s path seemed to clear-‐cut through thousands of
lives. Somewhere in that wreckage, Julie was making her way out of the debris. Forecasters
had begun their reckonings.
196 Huntsville-‐Madison County Emergency Management Agency, “Alabama Tornado Outbreak.” 197 Carlson, “Seeking Shelter: As Tornado Bore Down, Residents Flocked to Hospital.”
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Recovery. I find this word unsatisfying in the context of disasters. The notion that
we can return to what was before, to get back, as the etymology of the word suggests,
doesn't bear out in practice. People instead function at a “new normal” that can be more
difficult, less stable, and materially different than the previous one. Yet, some hazards and
disaster literature centers on recovery as one of the main aspects of what they call a
disaster cycle. Others rebrand recovery as resilience, what Kathleen Tierney defines as “the
inherent and preexisting qualities and attributes that enable at-‐risk entities to absorb
stresses caused by eternal shocks” combined with “adaptive or post event activities and
processes that enhance coping capacity.”198 Moving toward a new normal is indeed a goal
of communities, like Moore, that have been devastated time and again. But in the narrative
of recovery, and the attending subthemes of resilience, vulnerability, and justice, just who
recovers, who decides, and at what cost are rarely addressed within the discussion. And in
this literature, emphasis on the emotional recuperation of the community extends to first
responders, those like medical and law officials, fire fighters and police officers, as well as
emergency managers. But others fall outside the visible scope of community recovery.
One person in the audience listening to the panel raised this issue: “Are there
resources available to you all in terms of PTSD, or do you know if research has been
conducted on the effects of extreme weather on forecasters?”
It seemed an obvious question at the moment, but no one in the room could think of
any studies. One of the panelists responded that the agency had started making counseling
available to National Weather Service staff after 2011 when a series of deadly tornadoes
198 Tierney, The Social Roots of Risk: Producing Disasters, Promoting Resilience, 69.
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killed hundreds in the Southeast. I’d spoken with forecasters in forecast offices in Alabama.
Five years later, one person broke down in front of a crowd as he relived the days after the
EF-‐4 tornado killed 100 people in his area and injured nearly another hundred. He’d been
out with the damage survey team after working virtually non-‐stop for the past 24 hours in
the forecast office when they encountered a teenager sitting on the steps of a house that
had been swept away by the tornado. Nothing remained of his home. After talking to the
boy for a bit, offering him some of their lunch, the boy walked them across his empty
foundation, pointing out where he’d found his mother’s body, his father’s. “My wife says it’s
good for me to talk about what happened,” he said as he wiped away tears. “It is slowly
getting better.”
National Weather Service assessments of weather disasters written after the fact
offer a vivid description of the meteorological conditions that led to the storm, what
warnings were issued and when, what technologies failed and how, and what behaviors
were taken—by forecasters, decision makers, and members of the public.199 How could
Laura and I distill or even begin to include as a “best practice” the way Julie handled the
unique circumstances of her responsibility to the hospital and all who entered that day?
What problem might we identify in how forecasters negotiated their fear for their
communities against the trauma of accountability?
If I could have conducted my part of that assessment differently, I might have
encouraged those we interviewed to tell us about the things that trouble them as they
contemplate the vagaries of weather. What do they wish others knew about their lives and
199 National Weather Service, “The Historic Tornadoes of April 2011.”
95
the way they feel about that which they’ve survived? The questions would center less on
the collection of systems of risk in order to get to the personal and more on individuals and
their relationship to one another such that we might see the infrastructures from new
places. I would begin my intervention here, in the first person, holding up mirrors to reflect
the varieties of selves that come into view in moments of crisis. I offer myself as this
instrument and these words as to refract all that might be seen.
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Article 3: Weather Ready Nation or Ready Weather Agency? Developing an Ethic of Resilience in the National Weather
Service
Prologue
Over the last few years, at meteorological society conferences and in informal
conversations, there has been a lot of discussion about the future of the National Weather
Service. Some of these concerns arise out of continual discussion by some Congressional
leaders about the value of the agency in an era where private sector meteorologists might
better (and more cheaply for the government, they argue) create weather forecasts and
warnings. In 2005, for example, Senator Rick Santorum proposed the National Weather
Service Duties Act that many felt would eliminate the ability of forecasters to dissemination
their forecasts to different publics. He proposed to limit the function of the NWS to
warnings and alerts only to prohibit government competition with private industry 200. In
2011 and 2014, articles in national newspapers appeared, reviving questions about the
need for a national Weather Service, given that private entities can offer similar services201.
And as recently as 2015, U. S. Congressman Representative Jim Bridenstine from Oklahoma
included in his proposed legislation, The National Space Renaissance Act, that forecasters
in the National Weather Service be prohibited from doing what private meteorologists
might do, in his opinion, better: "Before commencing the development of any [forecasting]
program, the (NOAA) Administrator shall certify to Congress that no commercial capability
200 Sen. Rick Santorum [R-‐PA], National Weather Service Duties Act of 2005. 201 Gelman, “What Do Rick Santorum and Andrew Cuomo Have in Common?”; Murray and Bier, “Do We Really Need a National Weather Service?”
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or service, with or without reasonable modifications, can meet the requirements for which
such program is being developed." He put it more directly in a statement to the Committee
on Science, Space, & Technology on June 8, 2016. National Weather Service forecasters, he
wrote, should only provide “foundational datasets that others utilize to produce life-‐saving
forecasts, rather than duplicating efforts and technologies that are employed or could be
employed by the private sector.”202 Others argue in defense of keeping warnings in an
agency that can speak with “one definitive voice” about threats to the public, and do so with
more experience, local expertise, and “scientific rigor” than many employed in the private
sector.203
This parsing of duties between public forecasts offered at no charge by the National
Weather Service and the private sector’s products created for pay to customer
specifications is an old debate in many ways.204 Budget expenditures for staffing are
“bloated” some claim, noting that many areas of the country do not need forecasters getting
overtime pay for overnight shifts worked during fair weather. And possibilities for cheaper
technologies—super computers, satellites, automated observation systems—may means
the public may no long need to fund the government to provide this underlying data.205
Such pressure no doubt creates an imperative for the National Weather Service to engage
in a political economy of forecasting that shapes the agency’s efforts to prove the value of
their products and services.
202 Bridenstine, “Private Sector Weather Forecasting: Assessing Products and Technologies.” 203 Mass, “Do We Need Local National Weather Service Offices If We Have Weather Apps, Accuweather, and the Weather Channel.” 204 National Research Council, “Fair Weather: Effective Partnerships in Weather and Climate Services.” 205 Rosenfeld, “Do We Need the National Weather Service?”
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In response, Congress commissioned two National Academies of Science reports to
examine the staffing structure and possible inefficiencies that could account for or
substantiate such accusations. The first, Weather Services for a Nation: Becoming Second to
None, highlighted three recommendations: technological development and improvements
in data acquisition and Numerical Weather Prediction; evolving staff structures to “utilize
core capabilities and optimally serve the public,” including evaluating the number, type,
and arrangement of staff at local forecast offices; and identify “secondary value-‐chain”
services that might augment their own infrastructures.206 Through these efforts, the agency
might “evolve” its services to better meet the needs of society and to do so more efficiently
and cheaply.
The second report, Forecast for the Future: Assuring the Capacity of the National
Weather Service, elaborated findings about NWS operations and possible “frameworks for
the future.” It specifically highlighted the latest agency strategic plan, launched in 2011,
called “Weather Ready Nation,” noting that this document offered the agency an
opportunity to re-‐examine its current practices to “better align its resources and
operations” to meet the needs of this “new paradigm.” In particular, building relationships
with stakeholders and core partners emerged as a central recommendation, one that the
report noted is “a new approach for the NWS that embraces collaboration and seeks new
ways to create value beyond traditional forecasting activities.”207 Expanding their
collaborative network would move the NWS beyond products that are similar to those 206 Committee on the Assessment of the National Weather Service’s and Committee on the Assessment of the National Weather Service’s, “Weather Services for the Nation: Becoming Second to None,” 3–6. 207 National Academy of Public Administration, “Forecast for the Future: Assuring the Capacity of the National Weather Service,” Congressional Report (Washington, D.C.: National Academy of Public Administration, May 2013), 12.
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created within private industry, thus creating a value that would be unique to the NWS and
perhaps more impervious to attack. To move forward, the report suggested “the NWS
conduct an NWS-‐wide analysis” of its infrastructures, workforce, training plans,
technological systems, and communications strategies.208 In 2014, the National Weather
Service hired consulting firm McKinsey & Company to follow these recommendations with
results now available at society meetings and in private agency discussions with the
agency’s union, the National Weather Service Employees Organization.
It is in this context that the following article emerges. As forward looking
documents, the “Weather Ready Nation” strategic plan and the “Weather Ready Nation
Roadmap” together instantiate concerns over both the value of the National Weather
Service and its products, as well as efforts to make the agency more relevant through
relationships with government partners in the public safety sector. These relationships get
codified in the key Roadmap initiative, Impact Based Decision Support Services, or IDSS, or
those policies that determine which groups count as partners and the activities and
practices forecasters develop to build relationships. In many ways, a Weather Ready Nation
is also a ready weather agency, imbricating society and forecasters in a mutual effort to
survive their respective threats—one an external resilience of communities against weather
dangers and another an internal resilience of the agency against societal irrelevance amid
economic pressures. .
In 2014, a forecaster at an office where I had been conducting observations asked if
I’d be willing to serve as a subject matter expert in a series of webinars held within the
NWS that would give local offices a chance to highlight IDSS activities they had created.
208 Ibid., 13–14.
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These webinars, he said, would last two years. “They’ll give Headquarters a chance to see
what offices are doing for IDSS and what they should be doing.” I was relatively new to the
concept of IDSS but thought that participating in these webinars would give me a chance to
learn more about the initiative and, perhaps, to help shape its direction and the kinds of
people who might be invited to the broader discussion. After just a few webinars, it became
clear that IDSS currently functioned as a flexible and grassroots movement in the NWS.
Although administrators stressed the broader framework of IDSS as one that would build
relationships with partners, they weren’t sure yet exactly what IDSS would or should look
like. They were taking their cue from local offices and their example activities. The IDSS
conference portal, which is password protected for NWS and facilitators only,209 notes that
“The goal of the IDSS Webinars is to develop a place for our employees to share Impact-‐
based DSS (IDSS) examples and drive innovation, training, collaboration, consistency, and
coordination across all of the NWS.” Relevant to my interests in helping facilitate these
sessions, we were asked to “highlight all types of IDSS” and “spotlight IDSS provided to
emerging unique partners.”
But it is more than this. For individual forecasters, the collective Weather Ready
Nation paradigm offers an open question about who forecasters are and what they are
for.210 I examine the entanglement of Weather Ready Nation, IDSS, and resilience to make
this opportunity more clear. In the context of the other articles in this dissertation, my hope
is that this one demonstrates a range of images available to the forecaster that need not be
209 While the site is protected, all video recordings of IDSS webinars are available on YouTube , though they are difficult to find with a blind search. Each webinar has a unique url and there is no general category or channel to which they all belong. 210 Downey, “What Is Engineering Studies For? Dominant Practices and Scalable Scholarship,” 2009.
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mutually exclusive to images forecasters as accuracy experts but co-‐existing with care in an
ethic of empathetic accuracy. My contribution to the forecasting community, then, is a new
way to see themselves as capable of retaining scientific authority and expertise as
predictors but doing so in the service of their concern over the lives and wellbeing of their
communities. That is, empathetic accuracy has the potential to turn the science of
forecasting from a singular enterprise to a plural one, such that what it means to be a
forecaster may include the narrower emphasis on accuracy and elevate impact-‐oriented
communication as part of their science. While it is true that communication and
partnership building has been part of their practice across many contexts, I suggest these
efforts have been largely marginalized. Until recently, forecast offices, for example, had a
designated desk where a forecaster would answer phone calls from the public—a “public
desk,” as some call it. Others throughout the office answer the phone as needed, too, yet the
designation of a singular public desk compared to several forecasting desks, suggests
communication is less a part of the job and the science. And there has been just one staff
member designated to building those relationships with different partners in the public
safety community, the Warning Coordination Meteorologist. Further, the pull toward
accuracy as a dominant ethic is strong in the NWS and could mean that IDSS becomes yet
another system of metrics that offer quantified and economic justification for a profession
that ought to traffic in the care of people.
This article has been accepted to an edited collection assembled by Sulfikar Amir
who is an associate professor of sociology at Nanyang Technnological University in
Singapore. I joined several colleagues from across the international Disaster STS
community at a 2-‐day workshop in June 2016 to develop the collection, titled “The
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Sociotechnical Constitution of Resilience: Structures, Practices, and Epistemologies.” In the
abstract for the workshop, Sulfikar wrote about what he envisioned for us as a group:
“…while there are multiple perspectives linked to resilience, there is still a need for
understanding the structures, practices and epistemologies related to resilience in
sociotechnical systems as a unified concept.” In short, he explained, “We need to build a
multidisciplinary STS critique of resilience.”
Through a number of case studies and theoretical discussions, we focused on a
critical examination of the term resilience and the ways it is imbricated in the
sociotechnical through what our group identified as sociomaterial structures, informational
relations, and anticipatory practices. The book will focus on different kinds of disasters,
and ask questions such as, What makes society resilient? And, what have we learned from
large-‐scale disasters about the role of knowledge, expertise, and community in resilience
and how to improve them? Other STS scholars in the collective include Scott Knowles, Anto
Mohsin, Ashley Carse, Katrina Petersen, Steven Healy, Megan Finn, and several scholars
writing about Fukishima: Ryuma Shineha, Mikihito Tanaka, Kurniawan Adi Sputro,
Hyungsub Choi, Khota Juraku, among others. Another aim, then, is to move beyond Western
discussions of disasters to look at how concepts like resilience and vulnerability travel and
transform in non-‐Western contexts, such as in East and Southeast Asia.
Tentatively titled Bouncing Back: The Sociotechnical Constitution of Resilience, our
collection will be submitted for review to one of three presses: MIT Press, University of
Pennsylvania Press, or Palgrave McMillian. I submitted an early draft of this article and
have revised it based on that feedback, in addition to what I’ve received from my
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committee. I’ll submit a final draft of the article to Sulfikar in January.. The target word
count is 8,000-‐10,000 words and we’re aiming for a publication date sometime in 2018.
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Weather Ready Nation or Ready Weather Agency? Developing an Ethic of Resilience in the National Weather Service
[Word Count: 8,281] Communities, organizations, and government agencies have increasingly mobilized
the concept of resilience in disaster discourse to develop strategies that might help people
to return to a state of normalcy after catastrophes or disruptions, or adapt to ongoing
changes in their environments. Yet, as this collection of articles demonstrates, resilience is
a polysemous concept that eludes straightforward definitions in its deployment. Critiques
of resilience, as the introduction argues, require transdisciplinary approaches to
understanding the structures, practices, and epistemologies that emerge in its wake. An
important aspect of resilience that is less often addressed in disaster discourse is its
normative aims, or as Healy and Mesman (2014) note, “the resilience of what, for whom,
and at what cost or tradeoff…?”211 Such questions help us understand the motives and
consequences of efforts that might otherwise be deemed innocuous or remain hidden. They
may also help reveal moments of possible intervention. In this article, I use these questions
to examine normative dimensions of resilience as they emerge in the latest “roadmap” for
the United States National Weather Service titled “Weather Ready Nation.”212
Resilience performs in this document through a collection of activities and concepts,
practices and policies instantiated in a new initiative called Impact Based Decision Support
Services, or IDSS. Together these function as a sociotechnical infrastructure and guiding
framework for forecasters’ engagement with their various publics. A reading of resilience
through a normative lens offers insight into multiple valences of resilience, which I show
211 Healy and Mesman, “Resilience: Contingency, Complexity, and Practice,” 155–56. 212 National Weather Service, “Weather Ready Nation Roadmap.”
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inflects two categories of threats. One valence points to the external threat of “extreme
weather events,” which destroy lives and livelihoods. Resilience in this instance resembles
a more common use of the term in that the agency uses IDSS to build an America that is
“ready for” and “responsive” to threats of dangerous weather. Another valence reflects
threats internal to the National Weather Service from those who question the need for the
agency in the era of “big weather.” IDSS, then, is a strategy to bolster the agency’s relevance
and value in society. Each valence of resilience promotes different images of the forecaster
and who the forecaster ought to be in relationship to their professional identity, their
scientific enterprise, and their commitments to different publics.
I suggest that IDSS also has the potential to dramatically reshape the future role of
the National Weather Service forecaster in society.213 Asking normative questions about
resilience and its instantiations in forecaster practices and its implications for their local
communities allows me to reveal alternative ethics that might reshape a sociotechnical
imaginary214 of the forecaster, especially those that better align with their agency’s
imperative to protect lives. Like other STS scholars who reveal the ethical and normative
dimensions of technologies, 215 I believe the ethics of intervening sits alongside the
analyst’s assessment of those scientific or technological endeavors examined. In this effort,
213 There is an important distinction in the forecasting community between public forecasters employed by the U.S. government to collect meteorological information and issue free products and warnings and private sector forecasters who work in industry and charge a fee for their services. My work focuses only on the former, though an assessment of the later would be welcomed and is much needed to understand the intersectionalities of their practices and missions. 214 Jasanoff, “Imagined and Invented Worlds.” 215 Heidegger, “The Question Concerning Technology”; Doppelt, “What Sort of Ethics Does Technology Require?”; Jonas, “Technology and Responsibility”; Jasanoff, “Imagined and Invented Worlds.”
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I perform what Downey and Zuiderent-‐Jerak call “meta-‐activism.”216 To this end, I offer the
National Weather Service a normative suggestion for how I believe they might move
forward in its pursuit of a new role for the forecaster in the 21st Century. In particular, I
posit that the National Weather Service scale up and make visible217 an image of the
forecaster bound by a new hybrid ethic that I call empathetic accuracy. It is an ethic that
performs the interconnectedness of accuracy, care, and relationality already present and
important to forecaster work.
Why examine an initiative like IDSS and why now? Scholars from fields that
comprise Science and Technology Studies (STS), have long argued that nascent
sociotechnical systems flex and shift as their activities and practices are negotiated among
many relevant social groups.218 IDSS is no exception. Not yet a standardized practice, it is
malleable and thus open to critique and redesign—a clear reason to engage with it at this
time. However, as Winner notes, it is not just the processes leading to closure that should
occupy the analyst’s efforts. Instead, our work can, and should, pay attention to the
consequences of such practices, the possibilities, and the invisible and silent groups who
have no say in its production.219 In the case of IDSS, I suggest the new focus on a limited
group of “core partners” disappears the lay public, leaving them to develop relationships
216 Downey and Zuiderent-‐Jerak, “Making and Doing: Engagement and Reflexive Learning in STS.” 217 Downey, The Machine in Me: An Anthropologist Sits Among Computer Engineers; Downey and Dumit, “What Is Engineering Studies for?: Dominant Practices and Scalable Scholarship.” 218 Ravetz, Scientific Knowledge and Its Social Problems; Hughes, “The Evolution of Large Technological Systems”; Bijker, Hughes, and Pinch, The Social Construction of Technological Systems: New Directions in the Sociology and History of Technology; Latour, Reassembling the Social: An Introduction to Actor-‐Network-‐Theory. 219 Winner, “Upon Opening the Black Box and Finding It Empty: Social Constructivism and the Philosophy of Technology.”
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with forecasters largely by proxy through their public safety officials or perhaps not at all.
This article is based on ethnographic and historical work I conducted at three
National Weather Service forecast offices and my participation as a facilitator and subject
matter expert for eight IDSS webinars. The agency created this online platform as an
internal forum for agency meteorologists to explore and negotiate the definitions and
boundaries of IDSS. In what follows, I give an overview of the National Weather Service and
the concept of resilience; I then offer and overview of the Weather Ready Nation Roadmap
and three classifications of IDSS activities that differently deploy resilience; next I highlight
the external and internal inflections of resilience in the NWS; finally, I conclude by arguing
for an empathetic accuracy as articulated in new readings of IDSS and possible changes in
education, training, and collaborations with social scientists.
The National Weather Service and the Concept of Resilience
Whenever hazardous weather is likely to occur somewhere in the United States,
operational meteorologists located in one of 122 local National Weather Service forecast
offices across the country assess predictive information; create a host of “products” that
give spatial, temporal, and explanatory details about what might happen; and provide what
is called “services” (sometimes in the form of a product) to stakeholders and partners in
their community. During the immediate minutes during which storms form and weather
hazards emerge, NWS forecasters create specific products, called warnings, which are
alerts that not only classify and demarcate a weather threat but that give advice on actions
people should take. Since 1870, these activities have been a part of the agency’s mission “to
protect lives and property,” an ethical commitment that reflects their primary
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responsibility for issuing public weather warnings in the United States.220
National Weather Service warnings also represent an administrative and public
demonstration of forecasting skill, which the agency measures based on the correctness of
the prediction’s timing, location, threat type, and severity. In 1999, a National Research
Council report suggested that skill is a “quantifiable element of the forecast that
contributes to its accuracy,” one that allows for systematic comparison among forecasters
and over time. Accuracy, in this case, “refers to the general or unspecified predictive value
of a forecast or forecasting method.”221 That is, accuracy is important to forecasting only if
someone is able to use the prediction. In many ways, then, accuracy has functioned as a
dominant principle in the agency, motivating its infrastructural, sociotechnical, and
professional developments as evidenced by its appearance as a priority goal in agency
strategic plans.222 Radar and satellite networks, real-‐time observational instruments,
forecaster workstations, computer databases and computer models—together these
instantiate promises of accuracy to “improve” or “advance” warnings and drive
Congressional funding, policy changes, and forecaster practices.223
An emphasis on public service and decision support exists in agency reports, as well,
though it is less visible and more ambiguous. For example, in the 2005 strategic plan, one
220 National Oceanic and Atmospheric Administration, “History of the National Weather Service.” 221 National Research Council, “A Vision for the National Weather Service: Road Map for the Future,” 12. 222 “Weather Ready Nation: NOAA’s National Weather Service Strategic Plan”; National Weather Service, “Vision 2005: National Weather Service Strategic Plan for Weather, Water, and Climate Services, 2000 -‐ 2005”; National Weather Service, “Working Together to Save Lives: National Weather Service Strategic Plan for 2005-‐2010.” 223 Committee on the Assessment of the National Weather Service’s and Committee on the Assessment of the National Weather Service’s, “Weather Services for the Nation: Becoming Second to None”; The Weather Service Modernization Act of 1992.
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paragraph in twenty-‐four pages of text explicitly highlights users of weather information:
“As we focus on improving our services and expanding their scope,” the report notes, “we
will consult effectively with all who are affected by our services and be guided by our
customer’s needs.”224 As with other plans before Weather Ready Nation, this reference to
the agency’s public is often stated without much explanation of which resources will foster
and enable these relationships. Yet a variety of publics are the ones determining the
prediction’s value. In a chart that correlates the strategic plan outcomes with forecaster
activities, for example, a list of technologies, such as radar and observation systems,
constitute the mechanisms for creating customer service. Thus, developing better, accurate
technologies and more robust infrastructures may stand in for relationships with their
users, or “customers,” as they’re frequently called. In this sense, National Weather Service
publics are cast as members in a business transaction rather than as participants in the
enterprise of protecting lives.
As with many kinds of disasters, those that involve weather become a moment of
possible transformation for institutions. In 2011, over 600 people died in two tornado
disasters in the United States. The first struck six states in the Southeast on April 27,
producing 363 tornadoes and 340 deaths.225 Dubbed the Super Outbreak, it was followed
the next month by another outbreak in the Midwest, which produced an EF-‐5 tornado in
Joplin, Missouri on May 22. This single but deadly tornado ripped through the town on a
Sunday afternoon, destroyed 25% of the town and killed 159 people, the deadliest single
224 National Weather Service, “Working Together to Save Lives: National Weather Service Strategic Plan for 2005-‐2010,” 6. 225 National Weather Service, “The Historic Tornadoes of April 2011.”
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tornado since 1948.226 Death tolls like these had not occurred from a tornado since
the1940s, well before current warning infrastructures had been put in place. Questions
posed by the weather community in the wake of these disasters centered on a problematic
of the public: How could so many die when the technology and science of meteorology has
advanced as much as it has? Why didn’t more people act on warnings? What could be done
to better communicate weather risks?
In light of such troubling problematics, the National Weather Service launched its
“Weather Ready Nation” strategic plan in late 2011 and a more detailed “Weather Ready
Nation Roadmap” in 2013 to help execute this vision.227 The Roadmap is a 75-‐page
document whose cover design includes two disconnected puzzle pieces superimposed with
images of people in a snowstorm walking on the street, holding umbrellas. Behind this
image is a faint watermark of a towering storm cloud, positioned as a threat looming
behind those in the puzzle pieces. From the outset, then, it seems that what follows inside
the Roadmap will point to individuals in the lay public who are at risk from dangerous
weather—it is a problematic to be solved. To this end the following pages outline
subdivided plans that “describe activities and milestones” 228 the NWS must meet to
successfully implement its vision by the year 2020, a solution that “will translate the
Strategic Plan into real-‐life actions that save lives and livelihoods.”229
Resilience: Generally and Locally
A primary function of the Roadmap, the authors write, is to articulate a plan to build 226 National Oceanic and Atmospheric Administration, “NWS Central Region Service Assessment: Joplin, Missouri, Tornado-‐-‐May 22, 2011.” 227 Furgione, Weather Ready Nation and Social Sciences. 228 National Weather Service, “Weather Ready Nation Roadmap,” 1. 229 U.S. Department of Commerce, “NOAA Strategic Priority: Building a Weather-‐Ready Nation.”
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“increasing community resilience for future extreme events.” 230 But what is meant by
resilience generally and within the pages of the Roadmap specifically?
On the larger scale, the politics of climate change, climate engineering, and more
recently, debates about the anthropocene, expose the mutual constitution of sociotechnical,
ideological, and imaginative practices about the future of human survival and the quality
and equality of life on a warming planet.231 As a science of uncertainty, climate research
builds on a scale that attracts collective attention from scholars, activists, politicians, and
scientists in the global north and south. In particular, strategies for mitigating and adapting
to climate change come to the foreground, with an increasing attention to environmental
and infrastructural resilience, in light of inequitable societal vulnerabilities.232 Concepts
like these, however, are not stable nor unanimous in their meanings or consequences and
so shift in how they are used, depending on the contexts of their deployment.
The relationship between resilience and vulnerability is particularly complex and
fraught. Traveling out of systems ecology in the 1970s, through various disciplines—
human geography, psychology, hazards literature, and disaster studies—and into
engineering and community planning, resilience arrives at each destination ambiguous and
multiple.233 Current framings typically cast resilience as a positive notion, one that
230 National Weather Service, “Weather Ready Nation Roadmap,” 5. 231 Lövbrand, Stripple, and Wiman, “Earth System Governmentality”; Haraway, “Anthropocene, Capitalocene, Chthulucene”; Clark, “Geo-‐Politics and the Disaster of the Anthropocene”; Hamilton, “Ethical Anxieties about Geoengineering”; Humphreys, “Smoke and Mirrors”; Hulme, Can Science Fix Climate Change? A Case against Climate Engineering. 232 Cutter, Hazards Vulnerability and Environmental Justice; Lavell et al., “Climate Change”; Bankoff, Frerks, and Hilhorst, Mapping Vulnerability; Blaikie et al., At Risk; Dilling et al., “The Dynamics of Vulnerability: Why Adapting to Climate Variability Will Not Always Prepare Us for Climate Change.” 233 Miller et al., “Resilience and Vulnerability: Complementary or Conflicting Concepts?”; Endress, “The Social Constructedness of Resilience.”
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proposes institutions, communities, buildings, ecosystems, people, and the like, should be
altered to resist or adapt to disruptions from stressors and rebound to a previously
“normal” conditions or equilibrium quickly. Within a system, those that face the greatest
losses, express an inability to adapt or recover, or are most susceptible to harm are
considered vulnerable and in need of strategies of resilience. Vulnerability, then, is a
“relational notion”234 to resilience, one likewise arising in the 1970s in disaster contexts to
highlight those least capable of performing flexibility or sites where weaknesses in the
system might leave it open to disruption. Together vulnerability and resilience constitute
an anticipatory philosophy, one of expectation of worst-‐case scenarios, though just which
threats one should prepare for and when the work of resilience should complete is
unclear.235 As such they also represent a crisis framework,236 one that has normative
implications, which authors Healy and Mesman (2014) suggest get “overlooked” amid the
“imprecision” of its ambiguity. In the high stakes contexts of disaster work failures to
grapple with the ways vulnerability and resilience are constructed and their consequences
may result in body counts.
One such construction is the difference between climate change and weather
discourses that might affect deployment of term like resilience, a difference that is not
surprising given that the separation between the two sciences is tenuous and political.
Strictly defined, weather is the atmospheric processes and resulting phenomena that
materialize locally on shorter time scales (e.g. today through this week, or from the next
few minutes through the next seven days); climate is represented through a statistical 234 Healy and Mesman, “Resilience: Contingency, Complexity, and Practice,” 155. 235 Endress, “The Social Constructedness of Resilience.” 236 My thanks to Dr. Saul Halfon for this conceptualization of resilience, which highlights the “eventness” and urgency of weather hazards.
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analysis of the globe conveyed in a temporality greater than thirty years, though it may
stretch over several thousand years into the future or even into the past (e.g.
paleoclimatology).237 Yet they are intimately related, too. As Paul Edwards, noted historian
of science, suggests: “Climate knowledge is knowledge about the past. It’s a form of
history—the history of weather…”238
In the meteorological community, weather prediction is the domain of operational
and broadcast meteorologists who forecast the nuanced variables of what will affect people
locally in their day-‐to-‐day living; climate prediction involves researchers who simulate
possible futures at large temporal and spatial scales to study trends and patterns of
variation for the planet. While NWS meteorologists collect climate data—daily
observations that comprise climate reports that feed into larger systems of inquiry—
among these government, private, and broadcast meteorologists there is tension over the
cause of climate change, or global warming.239 Nor does consensus exist on how or whether
one can link extremes in weather to evidence of climate change. Weather, then, is seen as
largely apolitical and weather events as acts of God; climate change is politics all the way
down.
In terms of resilience, then, suggestions about how to deploy the term in climate
change scenarios and weather disasters differ on multiple levels, primarily timescales and
237 See the following for a comprehensive explanation of the differences and how they arose: Anderson, Predicting the Weather: Victorians and the Science of Meteorology; Edwards, A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming; Fleming, Fixing the Sky. 238 Edwards, A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming, xvii. 239 Stenhouse et al., “Meteorologists’ Views About Global Warming: A Survey of American Meteorological Society Professional Members.”
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lasting effects.240 Climate change is often thought of as a “slow disaster,” for example,
unfolding on a timeline of decades if not centuries.241 It likewise brings with it a deeper
uncertainty about the types of transformations one might expect not just locally but on
multiple scales. Resilience in climate change contexts, then, necessitates identifying the
global and local alterations that most likely represent a future earth.242 Because no one
scenario can be demonstrated to represent the “real” future, however, creating resilience is
also an activity of negotiating various scientific claims and political ideologies. This
includes the dismissive view that climate change is either a hoax or that we cannot know
which kind of future to adapt to, as well as the decisions abut how best to prepare.243 And
the dynamics of climate change shift in unexpected ways, making preparation much more
difficult to execute individually.244 Instead, climate change resilience involves entire
communities or regions.245 Thus, many people are left without a clear sense of just what
they ought to be preparing for now and how to begin.
While weather resilience overlaps in the sense that we cannot know just which
phenomena (e.g. rain, snow, tornado) will affect a community in a particular timeframe, the 240 Edwards, A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. 241 Erikson and Yule, A New Species of Trouble: Explorations in Disaster, Trauma, and Community; Nixon, “Slow Violence, Gender, and the Environmentalism of the Poor.” 242 Lowe et al., “Does Tomorrow Ever Come? Disaster Narrative and Public Perceptions of Climate Change”; Yusoff and Gabrys, “Climate Change and the Imagination.” 243 Bierbaum et al., “A Comprehensive Review of Climate Adaptation in the United States: More than Before, but Less than Needed.”; Edwards, A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. 244 Dilling et al., “The Dynamics of Vulnerability: Why Adapting to Climate Variability Will Not Always Prepare Us for Climate Change”; Field et al., “Climate Change 2014: Impacts, Adaptation, and Vulnerability”; Lavell et al., “Climate Change.” 245 Dilling et al., “What Stakeholder Needs Tell Us about Enabling Adaptive Capacity: The Intersection of Context and Information Provision across Regions in the United States”; Jankovic, Coen, and Fleming, Intimate Universality: Local and Global Themes in the History of Weather and Climate.
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shorter duration implied in weather suggests that they are “fast disasters.”246 A tornado,
for example, develops over the course of minutes and generally lasts just as long. Because
these phenomena occur regularly and are visible in news cycles and everyday experience,
they are more tangible and “real.” Preparation for particular phenomena occurs at an
individual or community scale, as responsibility for surviving a flood, for example, is often
framed as a personal responsibility (e.g. through flood insurance, preparedness plans, etc).
Recovery from these “events” typically takes much longer than the time the weather
phenomena takes to cause damage, but prediction and recovery—two key elements of
resilience—is divided among various actors, with the former being the purview of weather
forecasters and the latter, the purview of emergency managers, local government, and
individual citizens.247
Resilience in the Weather Ready Nation Roadmap is likewise difficult to nail down.
The initial language of the 2011 Weather Ready Nation report shifts the discourse of
weather warnings from a preoccupation with creating better, discrete products to the
communication and interpretation of uncertainty for others. It potentially puts specific
publics at the center of forecasting, alongside accuracy. While this shift is not entirely new,
an agency-‐wide commitment to focusing more explicitly on partners and communication
strategies is. Instantiated in an initiative called Impact Based Decision Support Services
(IDSS), it is part of what Deputy Director Laura Furgione called a “formalization of this
246 Braun, “Sorting Out Disasters: A New Case Study for Classification Theory”; Knowles, The Disaster Experts: Mastering Risk in Modern America. 247 Anderson, “Preemption, Precaution, Preparedness.”
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[relationship and communication] work” in the practices, activities, and metrics of
prediction.248
Broadly defined, IDSS is an infrastructure of practices and people, things and bodies
that materially affect the outcomes of warning strategies. While not explicitly articulated as
such in the Roadmap, much of the document is a working out of the institutional
vocabulary and sociotechnical apparatuses that might lead to successful implementation.
Metrics of success for such efforts are largely absent as are specific activities that
forecasters will use to perform IDSS with other public safety officials, which they call “core
partners,” or government and non-‐government safety experts. As the name Impact Based
Support Services implies, forecasters will focus as much on developing accurate products
as on understanding the multiple ways that local weather affects people, called impacts.
Support, then, suggests that forecasters will interpret these products with the decision
maker and the impacts that concern them in mind. While forecasters may assist decision
makers, I have observed that they only go so far as to describe probabilities, or likelihoods
for different scenarios, and articulate aspects of their skill: their confidence in a particular
probability, for example. They are limited, however, by official policies that prohibit them
from doing more than “ensuring they understand the information provided in […] products
relating to hazardous weather.”249 Any need to tailor information should be referred to the
private weather industry.
Key concepts underpinning the concept of IDSS are highlighted in section 1.2 of the
Roadmap. Important elements arise through an emphasis on relationship building in order
to reformulate forecasting practices around the notion of impacts, or the affect of weather 248 NWS Vision for IDSS. 249 National Weather Service, “NWS Support for Special Events,” 3.
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on people and structures. This requires a definitional flexibility and distribution of
expertise in deciding what constitutes an impact and by whom. Thus, forecaster, the report
notes, must understand “ what and how weather impacts a decision from the core partner’s
perspective” which then allows them to communicate their “uncertainty in understandable
terms.” Becoming resilient as an institution, then, requires forecasters to partially share
their atmospheric notions of risk with those articulated by partners who transform risk
into calculations that inform actions. Importantly, “NWS will evolve from a paradigm where
the forecaster generates products based on static definitions toward a services model
where the forecaster works closely with core partners to recognize their needs and provide
expertise to community decision-‐makers.”250 Forecasting in this model emphasizes concern
with others’ needs over the strict accuracy of their previous models.
Weather Ready Nation Roadmap: IDSS as Resilience
The Weather Ready Nation Roadmap articulates elements of resilience in each its
four plans: services, workforce, science and technology, and business. Each plan is intended
to help facilitate Impact Based Decision Support Services, and through it, multiple kinds of
resilience and I will discuss shortly. But first, I offer an overview of the plans through a
broad lens of resilience.
The Services Plan emphasizes the activities and skills forecasters will perform that
are oriented toward user decision making through the interpretation, communication, and
improved usefulness of weather information. Forecasters must “go beyond the production
of accurate forecasts and timely warnings and build in improved understanding and
anticipation of the likely human and economic impacts of such events.” IDSS is the
250 National Weather Service, “Weather Ready Nation Roadmap,” 11–15.
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“overarching paradigm” from which the NWS will deliver its services. As such, it becomes
an “obligatory passage point”251 in this plan, helping forecasters anticipate the “new and
evolving needs in society”252 and users access information shaped according to their risk
“thresholds.” Much of this effort is aimed at helping partners, or individuals in other
government public safety sectors, make effective decisions.
In the section on the Workforce Plan, the Roadmap highlights the forecaster role as
in alignment with the Services Plan the new and “evolved” NWS. Staff must be adaptive as
IDSS requirements modify, submit to new training, and become more diverse in their skills
to “meet the IDSS vision.” To this end, workforce adjustments must be made to reflect the
changing “American demographic” and the emergent concepts of IDSS. The forecaster, that
is, must be attuned to the people on the ground in order to better reflect their challenges
and concerns in their work. Yet, lists within the document mainly emphasize
communication skills and a host of technological training including GIS, computer
modeling, data visualization and “broader environmental science skills.” Likewise, training
involves physical sciences, “emerging science and technology,” communication,
management and leadership, and outreach. Nothing is said about how forecasters will be
trained to negotiate relationships or to understand partner needs. Still, these
technoscientific skills are meant to reflect not only the user needs but those of the agency,
too: “it is vital that NWS remain agile to keep up with the changing science and technology
and remain relevant to its evolving core partner and user requirements.”253 Together the
Services and Workforce Plans take more than two-‐thirds of the document. 251 Callon, “Elements of a Sociology of Translation: Domestication of the Scallops and the Fishermen of St Brieuc Bay.” 252 National Weather Service, “Weather Ready Nation Roadmap,” 6. 253 Ibid., 35–36.
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The last two plans Science and Technology and Business Plan are the briefest in the
Roadmap but highlight the scientific authority that forecasters bring to the prediction
process and that become part of the justification for the NWS value. Future technoscience
developments are focused around innovations and improvements in infrastructural
elements of the predictive process, especially computer models, observing systems, and
research to operations to research (R2O2R) test bed mechanisms. An emphasis on
“synchronizing societal impacts and environmental data” means developing the most
“precise and accurate environmental knowledge” that can be “delivered on demand” and in
forms that are “relevant to core partners’ preparation, response and recovery actions.”254
In fact, it is these efforts, the reader learns, that will help forecasters develop a
“comprehensive understand of the societal vulnerabilities” that make IDSS so potentially
important.
Together, these plans build toward maintaining the status of the National Weather
Service as a valuable government organization. But it is the Business Plan that sets out
toward this goal most explicitly. Its objective, the document says, is “deemed most
important to the future health of the organization,” a goal premised on the values of
sustainability, flexibility and agility, and an “increase in value” to the U.S. Together these
constitute the business model for the coming decade. It is in this plan where IDSS is
articulated most clearly as a mechanism that might help facilitate “good health” for the
National Weather Service, including helping the organization become more visible in its
communities through its partnerships. It is also in this plan that the official definition of
IDSS is stated: “NWS’ provision of relevant information and interpretative services to
254 Ibid., 48–51.
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enable core partners’ decisions when weather, water, or climate has a direct impact on the
protection of lives and livelihoods.” That is, IDSS will make more valuable forecasters
information since it is envisioned that it will directly help others make decisions that save
lives.255 By illuminating various concepts key to each plan, the Roadmap highlights the
mutual constitution of the social and technical in forecasting. It likewise reveals the
complex negotiations of resilience as embodied by the concept of IDSS.
IDSS is framed in through these plans as a linchpin of success in a larger effort to
“evolve the culture of the NWS.”256 Evolution, in fact, is an important framing of
resilience—the verb “evolve” occurs twenty-‐seven times throughout the Roadmap,
appearing several times in each section, indicating a systemic effort to change. And
evolution occurs on many fronts, from implementation of new tools and technologies,
workforce and staffing structures, to communication of information. While they are
discussed separately in the document, the three are mutually constitutive of a new National
Weather Service, one that is nimble, flexible, and ready to adapt—just like their publics.
“Local offices will evolve from product generators to expert decision support resources,”
the authors of the Roadmap write. “They will incorporate societal impacts to assist local
community decision makers and the public by focusing equally on production and IDSS.” In
effect, through its effort to evolve, the agency builds resilience, or a flexibility to respond to
outside influences and changing societal needs. Ideally, creating agency resilience allows
the forecasters to extend its benefits to their publics. But just which publics benefit is a
point I’ll examine shortly.
But what exactly is IDSS? Just as the official definition is broad and vague, in 255 Ibid., 65–68. 256 NWS Vision for IDSS.
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practice, the concept is slippery, expanding and contracting as local forecast offices
describe their situated understandings. In one local presentation, for example, IDSS
encompasses a larger number of NWS forecasting elements. The slide includes the
equation: “Science/Forecast + Partnerships / Outreach + Operations + Services Suite +
Training = IDSS culture. Several practices and people in the forecaster’s everyday world
add up to IDSS culture. Yet, they, themselves, are somehow outside this equation. This
equation is accompanied by a graphic that situates the word IDSS in the center of a circle
with the same five “core components” highlighted. IDSS in this case centers all aspects of
the forecasters profession, including their scientific practices. “IDSS is rooted in quality
forecasts based on sound science,” the presenter said of this slide. This one instance
illustrates the multiplicity of definitions and visions for IDSS and its relationship to
forecasting.
Nor does the Roadmap articulate exactly which activities the agency is willing to
count as IDSS. IDSS encompasses a range of activities, services, and products offered by
NWS forecast offices across the country. From my participation in the first national IDSS
webinar series hosted internally by the NWS for its own employees, I have observed a
diverse breadth and scope of practices that forecasters have labeled IDSS; however, most
fall into one of three loose categories.
The first include what I call dissemination IDSS, or those that require the forecaster
to repackage or explain weather information for particular users. The aim is to better
communicate expert knowledge of weather conditions and impacts and is often
unidirectional by design. Common examples include weekly webinars that offer
descriptions of upcoming changes in weather and possible effects on the community
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through graphical and textual PowerPoint slides. In the days leading up to dangerous
weather these are often coupled with conference calls held with local public safety officials,
such as principals of school district. .
Another category includes embedded IDSS, or those that necessitate a forecaster join
a partner or stakeholder group on-‐site to offer ongoing meteorological advice and updated
predictive information relevant to others’ needs. Perhaps the oldest and most familiar
example is the iMet, or incident meteorologist, who has trained in fire weather. These NWS
staff members become part of the local incident command team and are tasked with
keeping firefighters safe “by interpreting weather information, assessing its effect on the
fire and communicating it to fire crews.”257 While iMets are traditionally dedicated to fire
weather contexts, NWS forecasters are increasingly following the iMet model in other
hazardous weather situations, inserting themselves in emergency operation centers and
with other government entities.
The last category I call relationship IDSS, which differs from the last two in its
emphasis not on the meteorological expertise forecasters deploy but on the needs and
concerns of the people with whom they work. Relationship building exists in the other two
categories, but is not the explicit goal—it is an outcome or byproduct of the situation and
necessary interactions. Relationship IDSS, then, is not about giving predictive information
during or just before hazardous weather; instead, it is performed continuously throughout
the year and in ways that allow for an exchange of perspectives and a deeper
understanding between individuals about their roles and requirements in the warning
process. In this type of IDSS, the rapport built between people and the knowledge of 257 National Weather Service, “Eyes on the Sky: A Day in the Life of an Incident Meteorologist (IMET) on the Front Lines of a Wildfire.”
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specific conditions of their work shapes the practices forecasters engage in and the types of
predictive information they produce. It is this latter category that has the most potential for
highlighting the multiple ethics that exist in forecaster practices.
Although I have categorized activities to demonstrate that certain ones are
extensions of an older emphasis on accuracy and forecaster authority, for forecasters in the
IDSS webinars these activities are fluid in their practices. As one forecaster noted in a
presentation, the type of activity they select is relative to a request for IDSS from their
partners and to the significance of the impact of weather for their respective community. In
the graphic accompanying his talk, a “Pyramid of IDSS” illustrated his point. At the bottom,
in a wide green layer, were the words “Routine Briefings,” which he explained included
weekly webinars based on pre-‐formatted PowerPoint slides.
At the next, thinner green layer, was “Heads Up Email Support,” or notifications
about the possibility of dangerous weather sent out through a list serve several days in
advance; the middle orange layer noted “Conference Call and Range of Possibility
Graphics,” which he said, allowed them to visually and orally “communicate and explain
uncertainty” to their partners. The penultimate layer in pale orange said, “Video 2014,” a
reference to a local safety video, and at the top and smallest layer was “Direct Support,”
which included on-‐site and remote weather advice during public events. Along the right
hand side of the graphic was a black arrow pointing up, with the word “Impact” next to it.
He explained, they “prioritized their efforts based on where the request fell on this
pyramid.” Impact, however, might be the number of people involved in an activity that
required meteorological support or the political importance of an event (like a Papal visit),
such that it might be considered an issue of national security.
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I would like to return to Healy and Mesman and their call for those of us who
employ the term resilience to ask questions about the context and consequences of its use.
Within the scope of the Weather Ready Nation strategic plan and the activities and
practices generated by the IDSS initiative, I suggest that resilience is inflected primarily in
two ways: internally toward the agency and its staff and externally toward partners and
stakeholders. Distinctions between the two are often muddled in the execution of resilience
as the same practices might embody both internal and external articulations against or for
something or someone. It is a point made most apparent in tensions meteorologists express
over future practices that in which they will engage as the NWS transforms their work from
that which emphasizes accuracy to that which embodies resilience.
External Resilience: Ready, Responsive and Resilient
Within the pages of the Weather Ready Nation Roadmap, the imaginary of the U.S.
population is briefly constructed as one that is “ready, responsive, and resilient:”
NOAA’s Next Generation Strategic Plan establishes a long-‐term goal of a “Weather-‐Ready Nation,” as part of a broader vision of resilient ecosystems, communities, and economies. Weather-‐Ready Nation is about building community resilience in the face of increasing vulnerability to extreme weather and water events. In the end, emergency managers, first responders, government officials, businesses, and the public will be empowered to make faster, smarter decisions to save lives and protect livelihoods.258 (italics added)
To achieve national success, the Roadmap builds its argument for resilience at the smaller
scale of local communities, with specific emphasis on individual partners and decision
makers; motivation for this framing is not only an increase in extreme weather
occurrence—as one might expect in the discourse of climate change resilience—but a
growing vulnerability within the population and infrastructures to those extremes that
258 “Weather Ready Nation: NOAA’s National Weather Service Strategic Plan,” 1.
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happen each year. External resilience might thus be conceived of as for individuals,
especially vulnerable individuals, and is deployed against threats of dangerous weather.
Just who or what is vulnerable and the mechanisms by which vulnerability ought to
be identified is not clearly addressed in the plan. Vulnerability is an empty signifier in a risk
society259 where experts are not always able to recognize or identify who is likely to be
most negatively affected by weather disasters. Expanding the network of expertise to
public safety officials, they suggest, would enable forecasters to better visualize where
vulnerabilities exist and how choices might be made to reduce loss of life. The kinds of
resilience that ought to be built and where is likewise unclear, though the strategic plan
only hints at possible answers.
External resilience is likewise framed as a support mechanism for decision makers
who are responsible for taking actions on behalf of the lay public. By building meaningful
relationships with core partners, NWS forecasters create resilience against ignorance of the
circumstances, contexts, and needs or thresholds that need to be built into predictive
information. Similarly, these same core partners—emergency managers and other
governmental agencies, school districts, hospital administrators, etc.—manage resources
that are important to public safety. The assumption is that together forecasters and
emergency managers are both generating resilience against similar threats—weather
hazards, loss of life. On one level they are. Uncertainty is a common embodiment of
vulnerability, a threat that the NWS suggests might be mitigated through better
communication. Often defined as the range of possible weather outcomes and their
likelihoods, including worst-‐case scenarios and extremes, uncertainty shifts the
259 Beck, Risk Society: Towards a New Modernity.
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responsibility for decisions to those who receive such information. That is, only core
partners actually make decisions about community needs and so bear direct responsibility
for consequences of their efforts. Thus, Impact Based Decision Support Services, as one
government report notes, is an “enhanced, multi-‐disciplinary approach will empower
emergency managers, first responders, government officials, businesses and the public to
make fast, smart decisions to save lives and livelihoods.”260 But even these core partners
shift in practice as forecasters highlight the people who do not neatly fit this category, such
as organizers of large sporting events like NASCAR or those responsible for coordinating a
visit by the Pope to a large U.S. city.
Public forecasters explicitly refuse to join these partners in making the decisions—
they are part of the process but stop short of weighing in on the actual choice. As I’ve
witnessed many times, a forecaster’s support of a decision ends with providing their best
accounting of uncertainty. “That’s not my job,” is a common response to public school
administrators’ request for forecasters’ advice on whether or not to close schools early or
release buses filled with students. In one way, then, external resilience is also against a
threat of direct responsibility. How people choose to act on the predictive information that
forecasters provide is outside the scope of their role in society. Forecasters are implicated
in decisions that affect the larger community through the clarity of their explanations of
uncertainty, as well as their expressions of confidence for different ranges of possible
outcomes.
Internal Resilience: Weather Ready Nation or Ready Weather Agency?
260 American Meteorological Society, “State of the Weather and Climate Enterprise,” 2.
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In a presentation at the American Meteorological Society’s annual meeting in
January 2016, NWS administrators provided an update to the larger community about the
agency’s Operations and Workforce Analysis conducted by McKinsey & Company. The
presenter directly connected efforts to assess their organization with the goals of a
Weather Ready Nation and IDSS. As the title of her talk suggested, the anticipated report
would help in “Evolving the NWS to Build a Weather Ready Nation” through “actionable
ideas” that will change operational practices, staffing responsibilities, and organizational
structure. It would also offer a “set of skills so we can continually evolve over time to meet
the changing needs of society.”261 This echoes the Weather Ready Nation report with steps
in the agency to move forecasting toward interpretation and communication.
Internal resilience might be conceptualized as one that strengthens the agency, the
NWS itself, against the threats of irrelevancy raised by Congress time and again as private
sector companies threaten to displace the agency.262 Vulnerability, then, is not just a
characteristic of people in the public but one that applies to the health and vitality of the
organization. Resilience, as captured in the praxis of forecasting through IDSS, is about
building in flexibility for the institution such that it might evolve to meet the “changing
needs of society” in ways that make their services valuable. To meet these needs, one must
know them, and this necessitates a reorganizing and retraining of the workforce toward
such ends. Science and accuracy are still important, but resilience as relationship building
is on equal footing.
261 Swanson-‐Kagan et al., “Update on the NWS Operations and Workforce Analysis.” 262 Samenow, “Senate Bill Proposes Centralizing Weather Service Forecasting in 6 Regional Offices.”
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One challenge to this internal inflection is the response by individual forecasters in
the NWS, many of whom fear that IDSS represents a threat is a loss of scientific authority
within their own enterprise as they transition to interpreters of uncertainty. In my
fieldwork, several forecasters have expressed diverse reactions to what they perceive as a
loss of their scientific expertise in light of new initiatives like IDSS. As their traditional
operational duties have continually been scaled back, moving them into the narrower role
of the warning expert—a relatively small but important part of their daily operations—
forecasters have begun to ask what to do with their time at work if they don’t manage daily
weather prediction. They are accustomed to functioning as an authority in matters related
to prediction. As Daipha (2015) notes of this practice, “How disciplined improvisation is to
be transformed into a masterful weather forecast falls under forecasters’ sole
responsibility and discretion.”263 And it is in this accountability for the accuracy that
forecasters are most comfortable and where they find the passion and pride in their work.
Within a Weather Ready Nation, however, the role of the forecaster is projected to
change in ways that have been met with skepticism and frustration by individuals. To shift
their job from prediction to communication is as some have said to me, “waste forecasters’
scientific training,” situating them in the role of “hand holders” for their partners and
“translators” of science.264 To these forecasters, IDSS is a strategy of resilience for the
bureaucracy who must justify the value of the agency to Congress and the public; but to
their minds, it sacrifices of their skill and expertise. The tradeoff, then, is a devaluing and 263 Daipha, “From Bricolage to Collage: The Making of Decisions at a Weather Forecast Office,” 794. 264 Many forecasters dismiss IDSS and refuse to participate in activities that have an explicit connection to this initiative. As of today, administrators are allowing this choice with the belief that employee turnover and cultural changes made agency wide will eventually change attitudes and behaviors.
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deskilling of a profession and its members. In one reading of the internal resilience, the
answer to the question about just who resilience is for seems to be the experts, particularly
the NWS forecasters against threats from their own institution.
Within the weather forecasting community, many believe that regardless of
implications to the role of the forecaster, the kinds of resiliency efforts suggested in IDSS
practices will result in reduced deaths since forecasters will be better positioned to
represent their expertise to the people who depend on it. But will it? Is building
relationships with other experts, or “core partners,” in public safety enough to ensure that
the NWS administrators will not have to answer again the question about why so many
people died in a disaster in spite of current improvements in their sociotechnical
infrastructure?
A tradeoff of internal and external resilience unexamined in Weather Ready Nation
Roadmap is an implicit assumption by these experts is that an understanding of partner
needs will trickle down to offer protection to individuals in their communities. For this
reason, there is no “public IDSS” that extends to the lay public the commitment to develop
relationships and knowledge about the multiplicity and complexity of individual lives. I
argue there ought to be. Those outside the system of IDSS, especially those who rely for
their safety on NWS products, may face new dynamics of vulnerability from consequences
of resilience deployed. Yet in current formulations of IDSS they have been largely elided
from the weather forecasting structures that determine types and mechanisms for life-‐
saving alerts and the understanding that comes from deep relationships built over time.
Except through representation of those in the public sector or through research results
relayed by social scientists. Thus, bureaucratic initiatives like IDSS that reorient the policies
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and practices of individual forecasters and their relationships, attitudes, and actions
toward different publics embody this constitution of worlds and so deserve attention. The
scale of effects such practices have on lives exists both at the societal and institutional level,
but more importantly in this case, could exist at the individual level as forecasters from
local offices interact with people in their immediate geographical and County Warning
Areas.265
One potential consequence of this omission is that several new warning
infrastructures and tools are already underway that mimic the IDSS concept to some
degree, allowing partners to give input in defining hazards. As some interviewees creating
these technologies have suggested to me, these efforts are an improvement over old
models where the forecaster’s needs, criteria, and preferences dictated the kind of
information disseminated. Still, these newer efforts continue to position NWS forecasters
as authorities in issuing products that go out to the lay public in formats and mechanisms
untested and untried by a number of their various publics. There is no facilitation of
comprehensive and diverse input into the kinds of weather hazards and severity levels that
affect individual lives—or how forecaster practices might better reflect these needs.
Instead of embedding values derived from relationships with their communities, then,
these systems re-‐inscribe forecaster roles with only a gloss of a changed communication
strategies and only a partial fulfilling of their mission to protect all members of the public.
Conclusion: The Emergence of an Empathetic Accuracy
265 County Warning Areas are official jurisdictions managed by each Weather Forecast Office. In general, these CWAs were decided based on the scope of radar coverage in the 1980s and they cross statutory, political, and topographical boundaries.
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Leadership within the NWS note that they have dispersed “change agents”
throughout the agency to evolve the agency toward a “weather readiness” and carry out a
new “operational philosophy” that explicitly rejects a priority on accuracy as a mechanism
for the future in lieu of a priority on “deep relationships.”266 These individuals are likely
involved at the regional and local levels in helping to develop practices in concert with
IDSS, the initiative that most clearly has the potential to allow forecasters a meaningful
engagement with their core partners. Still, many of the ways that IDSS has begun to evolve
do not yet reflect the kinds of ethic necessary to be truly transformative. Currently,
forecasters tend to substitute accuracy for the goal of serving people.267 As scientific
experts, NWS meteorologists would need to create a profession trained and educated not
only to be caretakers of accuracy but to be caretakers of the people they serve, as well.
These two lines of care, however, are not mutually exclusive.
My critical participation in the weather community over the past five years has
compelled me to take seriously a normative obligation to identify problematics important
to forecasters and dominant images in of themselves within the forecast community.268 For
in shaping how forecasters see themselves and others, I am joining them in their
commitment to help protect lives by creating a system that reflects the needs and concerns
of people in harm’s way. Like others who have followed this normative turn in STS,269 I
believe my analysis ought to reveal to those with whom I participate—and to those who
266 U.S. Department of Commerce, “Evolution of the National Weather Service.” 267 Morss, “Problem Definition in Atmospheric Science Public Policy: An Example of Observing-‐System Design for Weather Prediction.” 268 Downey and Dumit, “What Is Engineering Studies for?: Dominant Practices and Scalable Scholarship”; Downey and Zuiderent-‐Jerak, “Making and Doing: Engagement and Reflexive Learning in STS.” 269 Cohen and Galusky, “Guest Editorial.”
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join me in disaster STS activist research—alternatives that reframe, scale up,270 and
reformulate future alternative roles for themselves and their practices in society. That is, I
view my activism as one of studying up271 in order to help NWS warning practices become
more attuned to and successful at meeting the agency’s ethical obligation to protect lives.
To this end, I offer the term “empathetic accuracy” as a way to reframe what seems
like a binary in forecaster work to better reflect the complex practices and relational
negotiations they encounter in their daily practices. I define the term as a hybrid ethic that
imbricates care, resilience and accuracy in the practices, attitudes, and materials
technoscientific experts develop for their publics. That is, empathic accuracy provides
forecasters with an ethic that focuses on predictive precision through their commitment to
a relational ethic with their publics. Based on principles underpinning Virginia Held’s ethic
of care,272 empathetic accuracy calls attention to the ways these values are imbricated in
their science, co-‐constituted in their technological developments and policies, and reflected
in the activities the direct their interactions with different publics. It is an ethic that
highlights relationships among people and the work of meeting the needs of others as
central to existence. In this sense, this hybrid ethic is not far off from the direction the
National Weather Service says it wants its enterprise to go. Yet this ethic goes further. It
makes public that desire, revealing in a new dominant image273 of the forecaster
relationships as a central premise to forecasting science itself and forecasting practices in
particular. 270 Downey and Dumit, “What Is Engineering Studies for?: Dominant Practices and Scalable Scholarship.” 271 Nadar, “Up the Anthropologist: Perspectives Gained from Studying up”; Priyadharshini, “Coming Unstuck: Thinking Otherwise about ‘Studying Up.’” 272 Held, The Ethics of Care: Personal, Political, and Global. 273 Downey, The Machine in Me: An Anthropologist Sits Among Computer Engineers.
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This suggestion to combine accuracy and care is not entirely new. In fact, an
etymology of accuracy in early sixteenth century Latin is accuratia or care or attention, as
in “executed with great care.”274 Perhaps somewhat fortuitously, then, the linguistic root of
the term accuracy involves care. Yet accuracy as a term is overly burdened by scientized
connotations about precision, truth, and objectivity, that its difficult to merely point to its
roots and have that suffice as a way of enrolling forecasters to consider care as an
important ethic in their labor.
So what would a world of forecasting look like should it scale up empathetic
accuracy? In the short term, I suggest it would include immediate and systemic
transformations in the education and training of forecasters. Then they might have, from
the very beginning of their careers, skills that match the expectation of a job based on both
on meteorological knowledge and public service and care. They might model their curricula
after those in medical professions that synthesize the two. A diversity of coursework would
include communication, for example, but not just that. Future forecasters would take
classes in the sociocultural and historical contingencies of meteorology, for example, spend
time grappling with the philosophical and ethical implications of their profession, and learn
more about the vulnerabilities that people face through on the ground volunteer work and
internships. In short, their education and training should help them be more engaged in
practices of care that are in line with these new job descriptions and expectations for
knowing how best to communicate with others.
In a longer term, deploying an empathetic accuracy could remake forecasters’
relationships with a variety of publics. Rather than a largely invisible enterprise that sees
274 “Accuracy.”
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itself as autonomous in its metrics and systems for accounting for success, the profession
and agencies of forecasting might engage their publics more directly and visibly. They
could hold public debates, enjoin different populations to offer input on technological
developments, and invite the communities to help them discover what science has
remained “undone”275 and thus, perhaps unintentionally, contributes to elements of their
work that facilitates the unjust. Vulnerabilities, for example, might be recognized and
addressed more quickly in their practices and missing publics might be identified and
made visible.
This alternative world might also lead to a greater sense of shared responsibility for
public safety, where forecasters and core partners are less segmented in their decisions
about public safety A Weather Ready Nation, then, might not be such a fragmented one,
with strict boundaries of politics driven by a political economy of prediction; instead, to be
weather ready would entail a unified effort, more in keeping with those narratives of
climate change that challenge clear distinction between public and expert in a world in
which everyone, like Beck’s risk society,276 is equally affected.
Finally, a new kind of science might emerge from this ethic, one that takes more
seriously the commitment to understanding individual needs. It would be a science in
keeping with the strong objectivity of feminist scholars who argue that better science
comes from below, from a multiplicity of standpoints, and by beginning the enterprise from
the people on the margins. As Sandra Harding points out, “The scientific/epistemological
275 Frickel et al., “Undone Science: Charting Social Movement and Civil Society Challenges to Research Agenda Setting.” 276 Beck, Risk Society: Towards a New Modernity.
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and ethical/political are inseparable in standpoint approaches to research.”277 The science
is remade from the outside in, with diverse perspectives blurring lines between expert and
public. It is through these kinds of revisions, I suggest, that the forecasters’—that our—
ethical commitments to society can best be fulfilled.
In this article, I have focused much of my critique on revealing that which may be
invisible to the weather community in concept of resilience as it is framed in their Weather
Ready Nation initiative and their IDSS paradigm. And I have argued that the concept of
resilience based on deep relationships with others offers the most promising way forward
for NWS forecasters in meeting their mission to protect lives—if it accounts for the
multiple valences and possible omissions such a term possesses. But it does not yet go far
enough. Resilience framed as deep relationships with multiple publics, from core partners
to everyday citizens, retains potential as a productive concept for a weather community to
uphold. Recognizing the problematic ways that current conceptualizations of resilience
functions in the infrastructure is a first step toward revising the system.
277 Harding, “Standpoint Theories: Productively Controversial,” 193.
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Article 4: Compromise and Action: Tactics for Doing Ethical Research in Disaster Zones
Prologue
Collaborative writing is common among disciplines in the social sciences, especially
where journal articles are the primary currency of academia and funding agencies. Yet in
the humanities, single authored monographs still carry the most weight and are thus the
standard. As a field, STS straddles humanities and social sciences, with faculty members
engaging in both kinds of publication types. Yet students are often trained through
coursework and the dissertation to only engage in solo publication practices as part of the
credentialing for the Ph.D. Even if students felt there were flexibility in collaborative
writing for the dissertation as there is in other disciplines, the challenges of learning how to
publish are significant for those who never have before, and finding collaborators who
understand the process and are willing to work with a novice scholar can be even more
daunting.
Since there are few people in the department who study what I do or who are
interested in co-‐publishing based on my interests in weather disasters, I’ve had to look
elsewhere for writing partners. One strategy I’ve found is to build relationships with
mentors in the meteorological and disaster community who might be interested in having
me conduct research and collaborate with them on publications. First, of course, is the
research community at NCAR where I completed a Graduate Student Visiting program in
2014-‐2015. I didn’t apply to this program blindly, however. I’d been building relationships
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with a few people there since 2012 when I met them at meteorological conferences.
Reading their work and learning about the kinds of research they did confirmed for me that
I could be a good fit for collaborative work. Since my time ended there, they’ve generously
let me continue working with them on a NSF funded project called CHIME (Communicating
Hazard Information in the Modern Environment: http://www2.mmm.ucar.edu/chime/).
To date, I’ve been included as one of multiple authors on a few sub-‐projects within the
larger group of physical and social scientists and through this experience I’ve learned a
great deal not only about collaborative writing but collective research processes.
Another collaboration happened in a more spontaneous way. I first met Max
Liboiron at my first 4S meeting in Copenhagen in 2011. Sumitra Nair acted as a mentor of
sorts for me at this conference and introduced me to many of her STS connections. A year
later, Hurricane Sandy struck the east coast where Max was attending school at NYU. She
and I reconnected and began to discuss the challenges of such complex disasters, her work
with Occupy Sandy and my work with the meteorological community. With another
colleague, Katrina Petersen, we started the DisasterCollaboratory website, a hub where we
envisioned scholars interested in working with communities affected by all kinds of
disasters might connect with people on the ground looking for resources and research.
That website stayed live for almost three years before we all became too busy to sustain it,
though we still have an active Facebook page. (My hope is to regenerate something similar
post-‐PhD). At the 4S conference in Barcelona, we talked one afternoon about the challenges
we’ve mutually encountered doing fieldwork in disaster zones and our frustrations with
feelings that we’ve come to recognize as “compromised.” The following article is one result
of that conversation.
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Max is an activist scholar and now an associate professor at Memorial University of
Newfoundland who exercises her feminist and indigenous politics in her research
(https://maxliboiron.com/). One commitment for her is that in discussing authorship of
the article, which we did before we began to write, she argued that she wanted lead
authorship to go to the scholar who needed the credit most, thus “exercising our politics in
our publications.” As a junior scholar, we decided I would take the lead even though she
spent more time helping me outline and organize the article toward our publication venue,
an edited collection “The New Environmental Crisis: Hazard, Disaster, and the Challenges
Ahead” (editors James Kendra, Scott Knowles, and Tricia Wachtendorf published by
Springer). While we each generated fifty percent of the work, Max took the lead in some of
our conversations over Skype about how to structure the article, how to frame our cases,
and the kinds of conclusions we might make.
We chose this collection for two reasons: First, Max and I know and admire Scott
Knowles, historian of technology and author of Disaster Experts. Together, we are also part
of a newer sub-‐group of scholars that arose after Katrina but found stronger coherence
after Fukishima; we are focused on theorizing Disaster STS (D-‐STS), a sub-‐field that will be
detailed in the new STS Handbook. We have worked together to create a D-‐STS website
(http://disaster-‐sts-‐network.org/) with resources and have put together sessions on D-‐STS
at various conferences, including 4S, SHOT, and AAA. To bring this community more
centrally into my work with the meteorological community, I invited Kim Fortun, Scott
Knowles, Vivian Choi, and Max to a workshop in Norman Oklahoma last May called “Living
With Extreme Weather” (http://extremeweather.ou.edu/). I sat on the planning committee
for this group and have as a goal infusing STS with more problems identified from within
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my research domain and bringing into weather hazards more STS scholarship. Our
manuscript has been accepted by the editor and has been revised based on a cursory
internal review.
The second reason we chose the edited collection is its stated goals, which closely
align with our own—or at least offer the opportunity for our D-‐STS goals to be visible.
From their call for proposals (https://sites.udel.edu/drc/files/2015/04/DRC-‐Call-‐for-‐
Chapters-‐1-‐qjqfzr.pdf):
How often do we come to the end of an article [on hazards and disaster topics] and see statements such as:
• We need more community involvement in decisions. • Culture and context need to be considered. • We need diversity in our research and initiatives. • Problems demand holistic, interdisciplinary, and socio-‐technical solutions. • Political will is necessary and people need to move out of dangerous places. • More models will help us and we need to fine-‐tune the message.
Too often, research ends with conclusions like these that either have had little success in implementation, or do not stimulate the transformative discourse that is necessary for us to do more in our field. We ask authors to push beyond these conclusions. We construe this book not as a standard literature review or a path to the same conclusions we’ve heard before. Rather, we see this as a source of guidance for future research, an assessment of present knowledge that can be useful to policymakers, a goad to action both in research and policy for students as well as those more established in the field. We are looking for a collection of essays, bold in their approach, that will differ somewhat in style and content from that expected in an anthology. We are looking for essays that will be theoretically rigorous but that will take surprising perspectives or tilt thinking in new directions. The aim is to be provocative and unexpected.
We wrote the following article, then, to connect our work to the D-‐STS community
and to offer a new way to think about disaster work conducted on the ground in light of
ethical dilemmas we have both faced. We also aim to be bold, perhaps provocative, in our
approach, style, and content—thus the use of graphics to illustrate different tactics we
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might take in our situated compromise. Our claim is that the context of disasters heightens
the need for researchers to consider first the ethical dimensions of their work and the
methods they select. Our opinion is that most research follows a process that is more
oriented around the problem definition and methodological considerations first with
ethical implications for the researcher and researched following from this. This is not true
of every researcher or every project, of course, but as our article explains, we both found
ourselves revisiting ethics after the problem and method had been engaged. We both felt
that our respective experiences demonstrated that what we’ve called the “high stakes”
outcomes of disaster research—namely potential fatalities and harm—creates a particular
kind of compromise for the disaster researcher. In many ways, it resembles concerns raised
by those who do community based participatory action research, as well as those who offer
criticisms of the IRB as a mechanism that is at once valuable and potentially dangerous. In
the end, such compromise generates a particular onus on the disaster research to consider
ethics of justice and relationality first and let methods and problem definition follow.
Part of the challenge in including a co-‐authored piece in my dissertation is that it’s
difficult for me to make wholesale or larger changes to the manuscript based on committee
feedback. I can adjust my case more easily and address language or concepts that need
nuanced revision. But rewriting the article toward different aims or renegotiating Max’s
work is much more difficult. This article represents a synthesis of our joint epistemologies
and ontologies—neither wholly one of us or the other. Still, I think the value of
demonstrating in my dissertation both a collaborative effort with the D-‐STS community
and my own ethics of relationality in context of the larger themes of my dissertation make
the challenges worth negotiating. To this end, I’ve contacted Max and she knows and has
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approved its inclusion in the dissertation and any changes to my own case. Other changes
we’ll negotiate together and with the editor, though the timeline for this revision falls
outside the timeline for my defense. Thus, I am including a version with track-‐changes here
that I’ll share with Max around my defense and then move forward based on her comments
and the editors.’ In many ways, this strikes me as a common negotiation of critique and
feedback from different reviewers.
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Compromise and Action: Tactics for Doing Ethical Research in Disaster Zones
Jen Henderson and Max Liboiron
This collection, New Environmental Crisis, seeks to go beyond the usual
recommendations that follow from disaster research, a call that mirrors a wider trend in
academic disciplines, including science and technology studies (STS), for action-‐oriented
research. Variously called making and doing,278 an engaged program,279 or a
reconstructivist agenda,280 the goal of action-‐oriented research in STS is to “improve the
effectiveness and influence of [...] scholarship beyond the field and/or to expand the modes
of [scholarly] knowledge production.”281 STS disaster research is particularly well suited to
this task because it attends to the externalizations of socio-‐technical systems that result in
high-‐stakes situations where we can potentially intervene to reduce harm and body counts.
Even outside of STS, most disaster research looks to create action that affects material
change on the ground, whether through triage, policy change, transformations to
infrastructure or management practices, or collaboration with communities.
Despite a cross-‐disciplinary push for what we collectively call action-‐oriented
research—a collection of practices that aim to move material conditions from an “is”
towards an “ought”—we argue that traditional research ethics and methodologies do not
help us navigate the contradictory positions we often find ourselves in when doing such
work. On the one hand, as disaster researchers we aim to account for modes of expertise, 278 Downey and Zuiderent-‐Jerak, “Making and Doing: Engagement and Reflexive Learning in STS.” 279 Sismondo, “Science and Technology Studies and an Engaged Program.” 280 Woodhouse et al., “Science Studies and Activism: Possibilities and Problems for Reconstructivist Agendas.” 281 4S, “STS Making and Doing.”
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representation, and political economy that are often discounted and disavowed in disaster
zones.282 On the other, if we are trying to effect changes in material conditions on the
ground, we are necessarily using the very modes of expertise, representation, and political
economy we criticize. As academics, we might well be able to provide a dulcet cultural
critique of the power relations inherent in top-‐down disaster relief, in the construction of
risk assessments, or in the assumptions of expert disaster communications. As action-‐
oriented researchers in the field who want the people around us to be warm, safe, and
healthy, we also need to engage with top-‐down disaster relief agencies, use risk
assessments, and listen to and convey expert disaster communications. That is, we work
within systems we have already deemed deeply problematic, or what activist-‐
anthropologist Charles Hale calls “compromised.”283
Hale argues that this contradictory position has positive effects for researchers: as
action-‐oriented researchers we are “inevitably drawn into the compromised conditions of
the political process. The resulting contradictions make the research more difficult to carry
out, but they also generate insight that otherwise would be impossible to achieve.”284 For
example, when canvassing New York City residents about their needs in the immediate
aftermath of Superstorm Sandy, community-‐based organizations found that data was
patchy and so would normally be thrown out if traditional sampling and data-‐cleaning
techniques were followed. Yet, using that same un-‐sampled and un-‐cleaned patchy data
painted a very different picture of the storm for residents residing in the disaster zone
282 Fortun et al., “Disaster STS.” 283 Hale, “Activist Research v. Cultural Critique: Indigenous Land Rights and the Contradictions of Politically Engaged Anthropology.” 284 Ibid., 98.
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compared to official accounts that “cleaned up” the data.285 This insight lead to academic
and policy papers that differed from official accounts.286 Even as we are aware of these
contradictions, tensions, and compromises, the question remains: how can we make
decisions that lead towards what we as researchers see as positive action from within the
“compromised conditions of the political process” we are seeking to change? How, in short,
do we do action while compromised? And how do we do so in a way that is driven by
research ethics that take into account the unexpected situations and high stakes so
common in disaster zones?
Most social science research about disasters is conducted based on an assessment of
the problem to be addressed (literature reviews and statements of problem) followed by a
deployment of methods appropriate to the research question (field or archival research).
Interviews, for example, often come with Institutional Review Board (IRB) requirements
for consent forms and to anonymize data. Ethics follow from methods. This is not to
suggest that ethics and methods are not mutually imbricated. But in traditional research
design, researchers often select methods based on the type of problem inquiry rather than
the ethical commitments they themselves have with the groups they’re investigating. We
argue that in action-‐oriented disaster research, it is the researcher’s ethical commitments
that should shape and refine methodological strategies and decisions. Ethics ought to drive
methods.
285 Liboiron, “Disaster Data, Data Activism: Grassroots Responses to Representations of Superstorm Sandy,” 2015. 286 Alliance for a Just Rebuilding, ALIGN, Urban Justice Center, Community Voices Heard, Faith in New York, Families United for Racial and Economic Equality, Good Old Lower East Side, Red Hook Initiative, and New York Communities for Change, “Weathering the Storm: Rebuilding a More Resilient New York City Housing Authority Post-‐Sandy”; Superstorm Research Lab, “A Tale of Two Sandys.”
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This chapter starts with two case studies of “compromise” during disaster research,
and then provides tactics for making decisions in the context of intractable problems
within compromise using ethics as a guiding principle. The two case studies are drawn
from our own experiences as disaster scholars and reflect two kinds of research: fast or
disaster-‐in-‐progress research and slow or post-‐disaster research. We use them to begin a
pragmatic discussion about how to be as ethical as possible as a generator and holder of
knowledge—a researcher—when institutional and employment affiliations, IRBs,
nondisclosure agreements, intellectual property agreements, pressures to publish or
perish, disclosure requirements to research offices and funders, and other binding
frameworks might imperil, under-‐serve, or replicate unjust power dynamics for people in
disaster zones.
In disaster zones, triage is immanent, not just in terms of the actions one might feel
obligated to take in the immediate aftermath of a disaster, such as helping people find
loved ones, or cleaning up homes. It also applies in the ways such a term can reframe how a
researcher prioritizes what she studies. Triage, then, is an apt metaphor for compromise, a
way of making decisions that involves an evaluation of priorities in the moment. It is highly
context dependent and involves responding to immediate needs as they arise, while at the
same time acknowledging that things are going to go wrong, or already have. And it is a
negotiation between less than ideal choices, guided by an overarching ethic.
One of our goals in this article is to articulate what ethics during research-‐triage,
and thus compromise, might look like. This first case study illustrates how the institutional
body that governs disaster research, the IRB, required one of us to develop a document to
clearly define the scope and stakes for research prior to arrival at a field site—a U.S.
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National Weather Service forecast office. That is, it required anticipatory ethics.287 Yet this
researcher’s experience in this particular disaster zone reveals a need for a more emergent
ethic that might permit a more complete story of what actually occurred to be told rather
than a story reflection the one researchers and the IRB predicted it might.
Ethics of Relationality: The Inadvertent Censure of Knowledge in Weather Disasters
As a disaster scholar, I work with expert forecasting communities who warn their
publics about dangerous weather by creating alerts, called warnings, that are transmitted
through broadcast media, websites, cell phones, and social media, among other
mechanisms. I’m especially interested in the sociotechnical challenges these experts face
when issuing warnings for high risk, high uncertainty weather, such as flash floods and
tornadoes. Rather than offer critique from outside the institutions, however, I have spent
fourteen months in three National Weather Service forecast offices observing and
interviewing meteorologists and their stakeholders with regards to their weather warning
practices. By “studying up”288 at key sites of power in the weather prediction community, I
am able to identify issues of urgent concern that have material consequences for those in
harm’s way. Some action-‐oriented researchers have called for “studying up” systems of
power rather than working only with those most affected by such institutions because it is
an ideal place to create change in larger systems.289
287 Elwood, “Negotiating Knowledge Production: The Everyday Inclusions, Exclusions, and Contradictions of Participatory GIS Research.” 288 Gusterson, “Studying Up Revisited.” 289 Nadar, “Up the Anthropologist: Perspectives Gained from Studying up”; Nygreen, “Reproducing or Challenging Power in the Questions We Ask and the Methods We Use: A Framework for Activist Research in Urban Education.”
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In this effort, I join scholars across the disaster research community who suggest
the warning process itself is beleaguered by multiple and complex challenges. Many in the
weather disaster community recognize we have insufficient knowledge about vulnerable
populations and their lack of capacity to access resources or information,290 as well as
omissions in addressing their unique needs in the context of warning technologies.291
Others have foregrounded problems in conveying uncertainties of hazard information292 or
definitional issues that complicate warning success.293 Moreover, there is still a great need
to challenge how these weather “events” are framed by their atmospheric occurrence (e.g.
tornado) and, as such, lack an accounting of the sociopolitical underpinnings that shape
material conditions in the communities in which they occur (e.g. poverty).294 A valuable site
of intervention for disaster researchers, then, is a bureaucratic system where warning
practices and policies originate and are largely taken for granted. If transformed toward a
fuller measure of equity and equality, such interventions may have the potential to effect
systemic change. Yet, even if we are in the right place to effect change, in the actual practice
of action-‐oriented disaster research, the unexpected can call into question strategies for
conducting ethical research in these disaster zone communities.
290 Anderson et al., “Far Far Away in Far Rockaway: Responses to Risks and Impacts during Hurricane Sandy through First-‐ Person Social Media Narratives”; Gall, Nguyen, and Cutter, “Integrated Research on Disaster Risk: Is It Really Integrated”; Lazrus et al., “Vulnerability beyond Stereotypes: Context and Agency in Hurricane Risk Communication”; Phillips, “Crowdsourcing Gender Equity.” 291 Wood and Weisman, “A Hole in the Weather Warning System.” 292 Morss, Lazo, and Demuth, “Examining the Use of Weather Forecasts in Decision Scenarios: Results from a US Survey with Implications for Uncertainty Communication.” 293 Barnes et al., “False Alarms and Close Calls: A Conceptual Model of Warning Accuracy.” 294 Fothergill and Peek, “Poverty and Disasters in the United States: A Review of Recent Sociological Findings”; Knowles, The Disaster Experts: Mastering Risk in Modern America.
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During my fieldwork, for example, I observed an incident in which forecasters
missed issuing a timely tornado warning for a storm that struck a small Western
community, destroying several homes. The storm took the forecasters by surprise. While a
warning did go out to the community minutes later, this delay in detecting the threat went
largely unacknowledged and unexamined in official accounts. Instead, statistical measures
the NWS uses to quantify the success of a warning reconstruct the event in ways that mask
narrations of experience and complexities of power relations at work between experts and
publics.295 I know about the full extent of this instance because my relationship with the
meteorologists in the office, one based on mutual trust, created an opportunity for me walk
the damage path with them the next day as they recorded the severity of the tornado and
collected descriptions of the tornado from those affected by the storm. Compromise arose
in that context.
Because the IRB approved forms I had completed nearly a year before my fieldwork
only focused on forecaster practices, the university IRB representative I spoke to about my
dilemma warned, “Since your IRB didn’t cover individuals in the community who were
affected by this storm, you shouldn’t even transcribe any portion of the audio taken from
that day.” Instead, I should have created a more robust IRB protocol that entailed as many
possible permutations of research participants as I could imagine. The limitations inherent
in my own vision of what disaster research might entail, then, precluded me from building
a protocol that might be as flexible and messy as the disasters I might encounter. Yet, those
of us who conduct such work know that disasters occur when normal modes of life are
suspended. As such, surprises and unexpected issues are inevitable, which necessitates that
295 Porter, Trust in Numbers: The Pursuit of Objectivity in Science and Public Life.
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the disaster researcher be flexible and open to situations as they arise. Yet institutional
research ethics are anticipatory in how they assume risks, norms of consent, and notions of
benefit and harm into our protocols before we conduct research. What we anticipate,
however, is always exceeded by what is on the ground.
The IRB administrator went on to explain a few choices I had at that point: “You can
track down participants and get their permission retroactively,” she said, a task that I
explained was made impossible by their anonymity during our initial conversations. “Well,”
she said, “you can submit a revised protocol to the IRB director and ask that you be allowed
to make generalizations about the community response without citing individuals.” In all
likelihood, she explained, I would only be allowed to distill this particular public’s
experiences to a few sentences. I would lose the particularities, the richness, and the
nuance of what individuals shared and how this might reveal moments to open up possible
conversations between forecasters and their publics. While I value the purpose of the IRB
in minimizing harm to participants, I wonder what kind of harm the omission of these
voices might create for individuals in the future should the publics’ experience remain
hidden from view. My narrative of this disaster would remain one-‐sided, limited,
inequitable.
And while other examples of problematic warnings of this same sort certainly exist,
which might be used to illuminate issues between forecast offices and their publics, how
many of these were fully witnessed by a researcher who could write about both sides? How
many researchers have examined the lifecycle of a warning from its generation to its affect
on the community to its instantiation as a government metric? Many such qualitative or
subjective accountings around sites of controversy get erased in the very classification
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systems that organize them.296 “Next time,” the IRB administrator advised, “I would plan
for these kinds of issues in the original paperwork.” It is a lesson I take forward should this
scenario repeat itself again, but what of other scenarios I am unable to foresee? More
importantly, I have come to realize that although IRBs can be consulted after the fact to
seek permission for changes or unexpected developments, they are not nimble or time-‐
sensitive instruments.
In this instance, I found myself in a compromised system, where I owed my
presence in the disaster zone to the Institutional Review Board. But the rules set out by the
IRB meant that I couldn’t act the way I thought was right. So what else can I do?
296 Bowker and Star, Sorting Things out: Classification and Its Consequences; Porter, Trust in Numbers: The Pursuit of Objectivity in Science and Public Life.
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As the flow chart above illustrates, in a disaster-‐in-‐progress, ethical dilemmas can
arise from a choice of methods that may leave the researcher in the intractable position of
being beholden to multiple stakeholder groups with mutually exclusive expectations of
conduct. For example, in terms of reporting the full breadth of my knowledge, I can remain
silent but in violation of deeper ethical responsibility to communities in disaster, or find
loopholes or create strategies to reveal what has not been approved by traditional ethic
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boards. These loopholes may cause me to lose or imperil my job, potentially have to
abandon hard-‐won access to field sites, or lose the trust of my research participants.
What the chart cannot represent is the internal turmoil I felt as I entered the field
and experienced the unexpected. Knowing that my research could unintentionally make
complexities and injustices that surround tornado disasters invisible has made me question
my ability to transform those bureaucratic systems within which I’ve worked so hard to
build networks of trust. In disaster zones, relational ethics suggests that we build
reciprocal relationships “that are attentive to the social context of the research, the
researcher’s situatedness with respect to that context, and the responsibilities which
researchers and research participants have toward each other.”297 That is, I felt as though I
had split loyalties between honoring the ethics of the IRB, which limited my ability to tell
the side of the people affected by the tornado; my ethic of relationality with forecasters
guided by my critical participation298 with them over several years; and individual or
community demands for a promise of institutional accountability for public safety.
These personal consequences in no way reflect the scale of consequences
communities face in environmental disasters. In this disaster-‐in-‐progress no one died, but
they could have. In the future, they likely will. It is this fact that drives me to operate within
the expert weather and climate communities where structure might be systemically
transformed, but that simultaneously put me in dilemmas based on incommensurable
assumptions between my own ethics and the IRB. Other research communities share this
297 Brun, “A Geographers’ Imperative? Research and Action in the Aftermath of Disaster,” 204. 298 Downey, “What Is Engineering Studies For? Dominant Practices and Scalable Scholarship,” 2009.
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same feeling of compromise and have begun to mount a critique of the IRB to build
flexibility and adaptability into the system.
As many in who conduct Community Based Participatory Action Research (CBPR)
have argued, current IRB mechanisms are inflexible, in part, because they are based on a
medical conception of what it means to be human.299 They assume a particular kind of
asymmetry in power relations between researcher and participant,300 focus on individual
protection rather than community rights,301 and are often unfamiliar with the principles of
CBPR research in ways that violate beneficence and justice with regard to researcher-‐
participant relationships.302 Additionally, the promise of work conducted ethically as
adjudicated by IRB mechanisms may act as an unintentional barrier to holding institutions
accountable for their actions. Compromise, then, may be an outcome of competing
promises made across different scales and with varying institutional norms of
responsibility. I suggest that like CPBR, disaster research likewise requires an ethics that
can handle emergent cases, especially those with ongoing deep relationships between
participants and the researchers. Instead of university IRB officials requiring detailed
protocols articulated in pre-‐established forms and completed before fieldwork, for
example, researchers might be allowed to develop with their communities alternative
ethics reviews processes and forms of consent that are more in line with relationships built
during and after crisis. This kind of local community review might be created in parallel
299 Stark, Behind Closed Doors: IRBS and the Making of Ethical Research; Schrag, Ethical Imperialism: Institutional Review Boards and the Social Sciences, 1965-‐2009. 300 Boser, “Power, Ethics, and the IRB.” 301 Shore et al., “Relationships Between Community-‐Based Processes for Research Ethics Review and Institution-‐Based IRBs: A National Study.” 302 Brown et al., “Institutional Review Board Challenges Related to Community-‐Based Participatory Research on Human Exposure to Environmental Toxins: A Case Study.”
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with or, in some cases, in place of IRB oversight. This would allow the researcher and the
community to negotiate responsibilities, outcomes, and flexible practices not currently
available in IRB protocols. If this emergent nature of disaster research is not better
reflected in our institutionalized ethics, it will continue to leave the researcher—and their
communities—compromised. That is, will my compromised position as a disaster
researcher and my initial choice of methods harm the communities I care about?
Toxic Exposures: Community Consultation for Cases of Unknown Harm
In the example above, withholding knowledge was a primary ethical issue. In the
example that follows, sharing knowledge is the problem. I research marine plastic pollution
in Newfoundland in northeastern Canada. Plastics attract toxic substances and can absorb
up to a million times more of a chemical than in surrounding water;303 if you’ve ever had
curry or spaghetti and put your leftovers in plastic tupperware, the difficulty scrubbing the
orange colour out of the plastic is a manifestation of this material tendency to absorb oily
chemicals. In the ocean, when these plastics and their absorbed chemicals are ingested by
fish, accumulated industrial chemicals move into fish’s bodies.304 Most of these chemicals
are endocrine disruptors, which have been correlated with infertility, recurrent
miscarriages, feminization of male fetuses, early-‐onset puberty, early-‐onset menopause,
obesity, diabetes, reduced brain development, cancer, and neurological disorders such as
303 Mato et al., “Plastic Resin Pellets as a Transport Medium for Toxic Chemicals in the Marine Environment.” 304 Colabuono, Taniguchi, and Montone, “Polychlorinated Biphenyls and Organochlorine Pesticides in Plastics Ingested by Seabirds.”; Rochman et al., “Ingested Plastic Transfers Hazardous Chemicals to Fish and Induces Hepatic Stress”; Tanaka et al., “Accumulation of Plastic-‐Derived Chemicals in Tissues of Seabirds Ingesting Marine Plastics.”
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early-‐onset senility in adults and reduced brain development in children.305 Their effects
are hard to track because they are caused by other factors as well, and can only be
correlated with exposure in laboratory settings.306
Many Newfoundlanders, particularly those in rural communities, depend on fish for
sustenance and livelihoods, and it is central to culture throughout the province. Marine
plastics in food webs are a slow disaster produced by routine, rather than exceptional or
explosive, exposures to toxic chemicals. Rob Nixon’s work on slow violence describes these
sorts of disaster, as they are “neither spectacular nor instantaneous, but rather incremental
and accretive, its calamitous repercussions playing out across a range of temporal
scales.”307 If I find that fish species used for food in Canada are highly contaminated with
plastics (or their associated chemicals), then my research would describe a slow disaster in
progress, but it may also impact communities beyond the harm chemicals are doing.
This has happened before. Between September 1987 and September 1988, breast
milk was collected from lactating mothers who lived on the east coast of the Hudson Bay in
the arctic. Unusually high levels of polychlorinated biphenyls (PCBs), a known carcinogen
found in coolants, were found in their breast milk, likely due to the mother’s diets of marine
mammals that are often contaminated with the chemicals.308 Journalist Maria Cone
recounts that, “Before the data could be analyzed, and before people in the villages were
notified, the discovery leaked to the press. On December 15,1988, Toronto's Globe and Mail
305 Grun and Blumberg, “Endocrine Disrupters as Obesogens,” 8; Halden, “Plastics and Health Risks,” 179–94; Bergman et al., “State of the Science of Endocrine Disrupting Chemicals 2012: Summary for Decision-‐Makers.” 306 Liboiron, “Redefining Pollution and Action: The Matter of Plastics”; Langston, Toxic Bodies: Hormone Disruptors and the Legacy of DES. 307 Nixon, Slow Violence and the Environmentalism of the Poor, 2. 308 Dewailly et al., “High Levels of PCBs in Breast Milk of Inuit Women from Arctic Quebec.”
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published a front-‐page story, quoting an Environment Canada official saying that the Inuit
were so contaminated that they may have to eat beef and chicken and give up whale, seal,
and walrus. The Inuit were terrified and some stopped eating their native foods.”309 Breast
feeding also became taboo, which had long term effects on health and culture.310 I do not
want a similar incident to happen in Newfoundland in the case of plastics. If there are
toxicants in food webs from plastics, I want communities to be able to determine how the
information is presented and circulated rather than a top-‐down, stigmatizing, incautions
approach.
In addition to the cultural violence that withdrawing traditional foods would
entail,311 sociologist of disaster Kai Erikson warns of the tolls of chronic dread and vigilance
for those who live in contaminated landscapes “alive with dangers, a terrain in which [...]
benevolences of creation are to be feared as sources of toxic infection.”312 Likewise,
Communicating about Contaminants in Country Food: The Aboriginal Experience warns
that "[w]hether or not individuals are exposed to or actually ingesting injurious levels of
contaminants, the threat alone leads to anxiety over risks to health, loss of familiar and
staple food, loss of employment or activity, loss of confidence in the basic food source and
the environment, and more generally a loss of control over one's destiny and well-‐
309 Cone, Silent Snow: The Slow Poisoning of the Arctic, 114. 310 Cone, Silent Snow: The Slow Poisoning of the Arctic; Boswell-‐Penc, Tainted Milk: Breastmilk, Feminisms, and the Politics of Environmental Degradation. 311 Reinhardt, “Spirit Food”; Waziyatawin and Yellow Bird, For Indigenous Eyes Only: A Decolonization Handbook; Wiedman, “Native American Embodiment of the Chronicities of Modernity: Reservation Food, Diabetes, and the Metabolic Syndrome among the Kiowa, Comanche, and Apache.” 312 Erikson, A New Species of Trouble: The Human Experience of Modern Disasters, 155.
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being.”313 I am faced with the possibility that even my provisional findings may cause harm,
regardless of my intentions, caveats, and my overall research goal of working towards
environmental justice. This is not to say that publics panic when they learn of
contamination in their food or bodies; there are ample findings to the contrary.314 Rather, it
is to say that there are real types of harm that research findings can do, particularly in
disaster zones, and I am trying to figure out how to be accountable to those possibilities.
My job as a researcher is not to simply record, describe, and report slow disasters. I
am a community member in a slow disaster. This intersectionality is not
compartmentalized so my responsibilities in my role as a local citizen versus a university
researcher are mutually exclusive. I am always both at once, so cannot detachedly report
contamination while living, working, and eating in contaminated zones, especially in a
place where cod is so central to culture, and has been a primary source of food and
livelihood for settlers since colonization, and for Aboriginal groups before and after
colonization. In Newfoundland, cod is life. The cod fishery collapsed in the early 1990s315
and the government’s cod moratorium resulted in the largest job loss in Canadian history,
exacerbating the already high unemployment and poverty rates in Newfoundland.316 I have
been to diners in outport Newfoundland (they mostly serve cod) where the newspaper
announcing the moratorium is laminated to the wall. Cod has been through a lot here. The 313 Usher, Communicating about Contaminants in Country Food: The Experience in Aboriginal Communities, 113. 314 Brody et al., “Reporting Individual Results for Biomonitoring and Environmental Exposures: Lessons Learned from Environmental Communication Case Studies”; Morello-‐Frosch et al., “Communicating Results in Post-‐Belmont Era Biomonitoring Studies: Lessons from Genetics and Neuroimaging Research.” 315 Bavington, Managed Annihilation: An Unnatural History of the Newfoundland Cod Collapse. 316 Schrank and Roy, “The Newfoundland Fishery and Economy Twenty Years after the Northern Cod Moratorium.”
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stakes of telling Newfoundlanders of yet another threat to cod has potentially far reaching
effects for health, culture, and economics. So what can I do?
This flowchart, and the preceding narrative, make it seem as though I have a series
of well-‐defined decisions to make after thoughtful consideration, and I can choose between
different unifying ethics to guide me through the research. In reality, I had already used
traditional research methods to gather cod fish guts and had started dissecting them before
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I realized that if I found a high amount of contamination, I had a problem. I was over a sink
with the partially digested contents of a cod’s last supper running through my fingers when
I thought, “Holy shit. This might be bad.” A feeling of dread and foreboding stole over me
long before I could articulate the problem in the way I have described above, even though I
was already well acquainted with the breast milk contamination story and am well-‐attuned
to the importance of traditional foods to local cultures.
What happened? I finished the study. I found some of the lowest plastic ingestion
rates ever recorded (publication forthcoming). Before my students and I started writing up
the findings, I held a public meeting hosted in one of the fishing communities I gathered cod
guts from to discuss the research. The meeting was well attended, and the room was
palpably tense as I spoke about how marine plastics and contamination worked, and about
our methodology of gathering cod guts from local fish harvesters. The moment I shared our
findings-‐-‐that we’d found the lowest plastic ingestion rates ever recorded-‐-‐people’s arms
uncrossed, they started laughing at my jokes, and talking out of turn. Many meeting
attendees celebrated the low plastic ingestion rate of their fish. Yet a low plastic ingestion
rate is not a harmless rate. The problem of plastic ingestion is likely to get worse as
increasing amounts of plastics are produced and flow into oceans.
My decisions about publication and future research are still not as clear as the chart
above might indicate, and I realize there are a myriad of options that I have not anticipated
and are not on the chart. However, the chart does provide guidance, and gives me the space
to think about my options rather than automatically following methodological courses of
actions common to university research (data collection > findings > publish > repeat). I
intend to form a community-‐based advisory committee that recommends what kind of
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research questions are the most important to consider, what aspects of the slow disaster to
focus on, and how to best mobilize and disseminate our findings (or not). But even this
strategy does not avert compromise. In fact, it will put me in a more compromised position
if my university, my funders, and my advisory board disagree, which seems rather
inevitable given different priorities and values. What will happen if my advisory board
thinks I should not publish or disseminate findings? What about my responsibilities to
people who eat our fish? Thinking through, and even leveraging, the tensions that arise
when working from within an academic institution with research ethics that come from
outside forums is not new,317 but it is a key context through which to think about
compromise and action.
Living and Working in Compromise
Anthropologist Charles Hales makes a sharp distinction between cultural critique,
where “political alignment is manifested through the content of the knowledge produced”
and activist research (his term), where politics happen “through a relationship established
with [...] people in struggle.”318 The latter “requires a substantive transformation in
317 Elwood, “Negotiating Knowledge Production: The Everyday Inclusions, Exclusions, and Contradictions of Participatory GIS Research”; Halvorsen, “Militant Research Against-‐and-‐Beyond Itself: Critical Perspectives from the University and Occupy London”; Russell, “Beyond Activism/Academia: Militant Research and the Radical Climate and Climate Justice Movement”; Russell, Pusey, and Chatterton, “What Can an Assemblage Do? Seven Propositions for a More Strategic and Politicized Assemblage Thinking”; Saxton et al., “Environmental Health and Justice and the Right to Research: Institutional Review Board Denials of Community-‐Based Chemical Biomonitoring of Breast Milk”; Schrag, Ethical Imperialism: Institutional Review Boards and the Social Sciences, 1965-‐2009; Taylor, “‘Being Useful’ after the Ivory Tower: Combining Research and Activism.” 318 “Activist Research v. Cultural Critique: Indigenous Land Rights and the Contradictions of Politically Engaged Anthropology,” 98.
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conventional research methods to achieve these goals.”319 Action-‐oriented research has a
different context of ethical deliberation in which relational ethics—rather than non-‐
anticipatory ethics—is important. And so it requires different methods and methodologies.
Compromise occurs through this difference, such as when a disaster researcher has to take
account of different commitments to different parties.
In both of our case studies, researchers came to an ethical dilemma in the middle of
research in spite of previous experience in disaster contexts. This will continue, even if we
try to anticipate the unexpected. It is clear that a step-‐by-‐step guide for action-‐based
research in disaster zones is impossible because action is context dependent and every
disaster is unique. But ethics can carry across contexts and, once we know what the
“greatest good” or highest commitment in our work is, it can guide actions in a variety of
situations. We suggest that Disaster STS can be a leader in thinking through these issues
because of its high stakes; while many research areas include action-‐oriented research that
will put researchers in difficult positions, disaster research does so immediately and often,
making it one ideal context to investigate these issues.
This is not to say we should throw away IRB ethics. Our research ethics align with
the basic principles of justice, beneficence, and doing no harm; our methods will always
entail informed consent and the option for anonymity. But they can also foreground
alternative ethics. For example, action-‐oriented researcher Ernest Stringer foregrounds
pride (people’s feelings of self-‐worth), dignity (people’s feelings of autonomy and
independence), control (people’s control over their own researches, decisions, actions, and
insights, including data), and responsibility (people’s ability to be accountable for their
319 Ibid.
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own actions) in his work using Participatory Action Research methodologies.320
Community psychologist Stephen Fawcett calls for guiding values of collaboration,
experimentation, and sustainability, among others.321 Action-‐based research, particularly
in disasters, comes from a different context than the medical context of institutional ethics
and so understands the subjects,322 methods,323 and goals324 of research differently from
those within which the IRB developed.
We argue that ethics should drive choice of methods, not the other way around. If
our greatest commitment is solidarity with vulnerable populations325 or social
movements,326 for example, then these ethics will dictate whether and how we do
interviews (paid or unpaid, collaborative or top-‐down), surveys (community-‐built and
conducted, or not), how and where we draw our samples and the overall the boundaries of
our research site.327 It will determine how (and with whom) we will make decisions when
the unexpected occurs. One possibility, for example, is that after researchers think about
their own ethical commitments and design their research accordingly, they have them
reviewed (formally or informally) by community groups, since one way to tell if your ethics
are just for people in disaster zones is to have them adjudicated by those on the ground in a 320 Stringer, Action Research, 23. 321 “Some Values Guiding Community Research and Action.” 322 Denzin and Giardina, Ethical Futures in Qualitative Research: Decolonizing the Politics of Knowledge. 323 Schrag, Ethical Imperialism: Institutional Review Boards and the Social Sciences, 1965-‐2009. 324 Lewis, “Ethics, Activism and the Anti-‐Colonial: Social Movement Research as Resistance.” 325 Nelson et al., “‘Nothing about Me, without Me’: Participatory Action Research with Self-‐Help/Mutual Aid Organizations for Psychiatric Consumer/Survivors.” 326 Situaciones, “On the Researcher-‐Militant.” 327 For an example of how sampling techniques can be tied to justice problems in disaster zones, see Liboiron, “Disaster Data, Data Activism: Grassroots Responses to Representations of Superstorm Sandy,” 2015.
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kind of ethics peer-‐review. We have attached a Memorandum of Understanding (See the
Appendix) for research in disaster zones developed in consultation with both researchers
and disaster survivors in the aftermath of Hurricane Sandy. It was designed to address
many of the ethical dilemmas and mistakes experienced by both groups, and draws heavily
on tribal research ethics, where research ethics have received substantial scrutiny and
revision that go far beyond what IRBs require. This is not the end of a process of peer
reviewing ethics, however. It is ongoing. There are many other possibilities for how to
enact research ethics in disaster.
In an important sense, the process of research is a form of action. We are not the
first to suggest that data collection is a process of negotiation where our collection
techniques have effects in the field. We can arrange our methods so they aim to make
positive change at all points in the research process, rather than only at the end once
findings are achieved.328 Moreover, it is not only action-‐oriented researchers in disaster
zones who navigate compromised systems; in many ways, compromise is not a choice for
any of us who produce and hold knowledge. Even those who don’t grapple with ethical
dilemmas are compromised because we all are always already participating in a system of
power.329 One of the premises of STS is that there is no outside of politics for research,
scientific or otherwise. The nature of disaster research makes this especially visible in our
own work, and invites us to be accountable to it.
328 Stringer, Action Research. 329 Rose, “Situating Knowledges: Positionality, Reflexivities and Other Tactics”; Kobayashi, “Coloring the Field: Gender, ‘Race,’ and the Politics of Fieldwork.”
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Acknowledgements
Funding for this research was provided by: Social Science and Humanities Research Council
(SSHRC) Insight Development Grant (#430-‐2015-‐00413); Marine Environmental
Observation Prediction and Response Network (MEOPAR); the Advanced Study Program
Graduate Student Visitor Fellowship at the National Center for Atmospheric Research; and
the Interdisciplinary Graduate Education Program in Remote Sensing at Virginia Tech.
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Conclusion Last week, I had the opportunity to present my work at the National Academy of
Sciences in Washington D.C. for a report called Advancing Social and Behavioral Science
Research and Application within the Weather Enterprise. It was a humbling experience on
many levels. My colleagues invited me to talk to them about the kinds of research related to
forecasters that I felt might be missing from current social science agendas. I had several
suggestions, of course, and I highlighted aspects of forecaster practices, experiences,
vulnerabilities, and technologies that we understand so little about. More importantly,
however, I presented my case in front of different audiences, including administrators from
the National Weather Service and a few representative forecasters from the larger weather
community.
After my presentation, a few members of the NWS approached me and asked about
my dissertation. What was I doing? What were my findings? What had I learned?
“I’m looking at the ethical dimensions of NWS warning practices,” I said. One man
raised his eyebrows. “Ethics?” I explained the types of values I’d explored in my work,
accuracy and the man-‐machine mix, care and concern during moments of crisis. But before
I could get to my conclusions, my contribution to the world, I could tell he was confused.
“So you’re looking at little “e” not big “E” ethics. So things like verification, accuracy, skill….”
He trailed off. From other conversations of this sort, I suspected that an underlying
question for him was this: How will this help us be better forecasters?
For just a few minutes, I panicked. Not because I don’t believe in what I’m doing or
feel strongly about my efforts to theorize a new image of the forecaster, one that arises
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amid what I have called empathetic accuracy. I feel strongly that this concept is important
to forecasters on many levels. It could help them re-‐see their science via an accuracy
developed through a deeper engagement with their publics. It’s a concept that could
assuage their anxieties over being replaced by machines since this newly cast accuracy
through care accounts for more than what comes from competing against computer
models. And as an organization, the NWS might see different ways to educate and train
forecasters to be better suited in their new roles as relationship builders with partners and
with their communities.
Yet, I realized as I conversed with this man that forecasters might reject my ideas.
They might find my critique and my suggestions unrealistic, untenable, or worse, useless. I
wondered, how can I convince them of my ideas now that they have been argued on paper?
What examples and analogies might I draw on? Will it be enough to write shorter
summaries of my dissertation for their publications? What will I present at their
conferences? These are questions I must grapple with as I move forward and continue my
engagement with this community. I imagine that it is through trial and error, and a number
of lengthy conversations with colleagues, that I’ll be able to begin to make progress.
But first I feel I ought to understand what some of the grounds are for rejecting, or
even co-‐opting, an ethic of empathetic accuracy. I’d like to spend just a little time thinking
“against the grain” of my own work to generate—or anticipate—how it might “travel” or
fail to do so. Please indulge me for just a few more minutes:
1. Empathetic accuracy might fall flat as too emotional, too much about love and “non-‐
scientific” concerns. In short, it sounds feminine.
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The language of empathy is one I’ve advocated for before. In a presentation at the
American Meteorological Society a few years ago, I talked about care as an important
dimension of forecaster work. I argued that their understanding of partners, their desire to
know more about them, comes from a place of love. This presentation, jokingly dubbed “the
love bomb” by my friends, had mixed results with forecasters. Some were moved by my
argument; others respectfully told me to talk about passion, not love. “We’re passionate
about our work,” one said. “But as ISTJs” (a reference to the most common Myers-‐Briggs
personality type in the NWS) “we’re not motivated by feelings.” I’ve had this kind of
conversation a few other times. Love, emotions, care, concern—these do not immediately
resonate with forecasters as values they, as scientists, ought to cultivate or recognize. It
sounds like I’m asking them to conform to stereotypes of being a female, a gendered way of
thinking about science that situates masculine as emotionless and thus appropriate to a
rational enterprise. The challenge for me will be to find the language forecasters already
use, and examples, cases, and analogies, that will appeal to them. I suspect this work will
involve discussions about their labor, their losses, and the contexts and outcomes of
weather disasters.
2. Empathetic accuracy might be seen as too simple an image for the forecaster, one
that presents them as “good guys.”
Offering an image of the forecaster as one whose accuracy comes through caring
might seem as though I’m looking at the profession through rose-‐colored glasses. The
forecasters in my work are good, hardworking people, perhaps represented as more two-‐
dimensional than three. Where is the complication, some might ask? It’s true that the
image I propose is partly an ideal, but it also reflects the realities and lived experiences of
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forecasters based on years of observations and conversations with them. Empathetic
accuracy reveals a fuller range of the complexities that comprise forecaster identities. It is
also one that rivals the dominant image, which I see as a much more simplified and
idealized image of the forecasting scientist primarily as predictor. This is a one-‐
dimensional image in how it invokes a largely invisible cadre of professionals working
behind screens to get the forecast and warnings correct and to do so according to narrow
policies and procedures. An ethic of empathetic accuracy offers a richer, multifaceted
professional image of what forecasters already value and perform. And it doesn’t subtract
from what they value as scientists.
3. Empathetic accuracy might not be measurable or bureaucratically feasible.
Many at the NWS might ask, as some already have, how do we measure empathetic
accuracy? Empathetic accuracy, I suggest, is not amenable to standard practices of
measurement and quantification. Assessments of relationality can arise from
understanding motives, practices, and values, and through engaging in reflexive activities
that ask whose needs are (not) being met and why. In many ways, the forecasting
community already relies on qualitative accounts (e.g. anecdotes) to support changes in the
system. I understand the bureaucratic responsibility to demonstrate success along certain
metrics, especially since the agency is responsible to Congressional oversight and
budgetary demands. I don’t deny this reality. And it may limit how this concept is deployed
in forecast offices. Still, I envision that together, we might develop local mechanisms of
assessment that draw on qualitative evidence and that represent the various dimensions of
what might count as success. Importantly, I hope that this new concept allows the weather
community to move beyond, or at least complement, statistical metrics that are already
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shown to be problematic. And I hope it helps trouble tacit metrics that keep the community
focused on their failures as a measure of losses in terms of body counts and dollar amounts.
4. Finally, empathetic accuracy might be co-‐opted as a way of justifying efforts or
initiatives that I critique. Or empathetic accuracy might “go wrong” in practice, negatively
affecting forecasters or their efforts at protection.
Some may think that encouraging forecasters to be more empathetic could create
conditions in which the emotions of care might interfere with the science or objectivity of
forecasters. To them, I would say this care already exists and affects decisions,
relationships, and outcomes. What we need is to talk openly about care and examine how it
currently functions and ought to function at various scales of practice. What are the
variations of care? Where does it emerge most strongly? How can we cultivate it or
challenge its limitations? My sense is that we can’t continue to ignore or downplay the
humanity of forecasting, for this will have consequences for the health of those who feel the
strain of concern that goes unacknowledged and unaddressed—as guilt and sadness, as a
manifestation of health related issues, such as post traumatic stress, and as it shapes hyper
vigilance or apathy in future disaster contexts. Care, then, is an obligation that ought to
emerge from the relationship between the NWS as an agency and its staff.
I am not the first to suggest that science ought to be integrated with empathy.
Unbeknownst to me, Thomas Friedman recently put forward the notion of STEMpathy, or a
way of connecting sciences and engineering with the social skills drawn from the
humanities. As one article that summarized a speech he gave on the subject in 2015, notes
Computers and algorithms can be trained to do almost anything, though they have no inherent social and interpersonal skills. Therefore, being able to work in STEM
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areas and also being able to communicate and collaborate with other humans is a key advantage for individuals entering an increasingly competitive job market.”330
STEMpathy is an economic future-‐making, a way of framing a solution to a competition
with machines that has automated people out of their jobs, or displaced them in other
ways. Is this what I mean? Is my concept about creating more employable forecasters? In a
way, yes. I hope to affect the education, training, and work of the forecaster so that it better
matches their goals of protecting lives. They need these skills to be better protectors and to
do so ethically. But mine is not a political economy of forecasting, though I see how it could
be used to those ends. Instead, because my dissertation derives its motivation from
feminist theories, I am more concerned with the creation of a better science based on a
multiplicity of perspectives gained through meaningful and trusting relationships. It is the
relationships themselves that inform forecast and warning practices and allow forecasters
to attend to a variety of needs. I am not concerned with improving employment chances
but evolving forecaster science through experts’ relationships with their publics. It is my
hope that by continually working with this community I can keep this concept from being
used to other ends—or at least challenge such uses when they occur.
I’m sure my work has many other limitations that I haven’t yet encountered. But I
like to think these can be addressed or at least explored in meaningful ways. Moving
forward from this dissertation, I’ll continue to critically participate with the weather
community to figure out ways to address our collective concerns. It’s not my place to solve
these problems but to work with forecasters and offer insights that enable new ways of
seeing a problem’s contours and boundaries—or what those problems may hide. This
330 (http://www.cobrt.com/education-‐workforce/11/4/2015-‐thomas-‐friedman-‐on-‐stem)
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requires a lot of listening, brainstorming, theorizing, and then acting together toward that
future.
I didn’t get to explain the concept of empathetic accuracy to my colleagues at the
National Academies last week. We didn’t have enough time and I didn’t feel confident
enough in my explanations to re-‐engage the subject. Moving forward from today, however,
I am enthusiastic about translating my dissertation into languages, examples, and
publications that will resonate with forecasters. And I hope to contribute my efforts to the
larger Disaster STS community in mobilizing STS insights toward helping practitioners and
first responders effect systematic change; and I hope that my travels among forecasters
shifts the Disaster STS community toward a greater attention to the effects of weather, its
disasters, and its expertise on society.
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Appendix Template of Memorandum of Understanding for Mutual Aid Research in Disasters
Superstorm Research Lab & Disaster Collaboratory A memorandum of understanding is a document designed to coordinate expectations and procedures between groups. It is useful when two groups that have not previously worked together. There are various uses of a memorandum, and the specific purpose is determined by the parties involved: it might be used to indicate good will on the part of both parties or to help them keep track of what they've agreed on. The agreement can be used to help to clarify the relationship between two organizations and to make clear which services or responsibilities each is responsible for. It can also set out clear decision making procedures and approaches to getting work done. It might help to supplement legal documents created with a university or business partner, but it is not a legally binding contract itself.331 When drafting an MOU, keep in mind the purposes of the agreement. The MOU should be detailed and comprehensive enough that each partner has a clear understanding of the collaboration or partnerships, their role in it, what is expected of them, and what they can expect from the rest of the group. It should also be broad and simple enough to support a nimble, adaptable collaborative effort. That is, the MOU should support the work of the collaboration, not get in the way. Most importantly, the MOU is a framework for ethics; the research ethics supported by academic Institutional Review Boards (IRBs) do not cover many types of challenges encountered in innovative research, collaborations, and unique populations or situations (see, for example, Denzin and Giardina 2007). Thus, it is up the collaborators to define the terms, scope, and elements of the work. The MOU provided here is a template to help you start your discussions. It is designed to be a resource for a mutually beneficial researcher-‐community or academic-‐activist partnerships. It covers a number of different types of collaborations and partnerships, as well as various issues that might need discussion; it is neither a mandatory nor comprehensive list of ingredients but is meant as a starting point for discussion. In fact, some items in the template are contradictory to others, anticipating a range of possible frameworks and philosophies of collaboration. At a macro level, it is modeled after Tribal Research Ethic Codes, community-‐based participatory research (CBPR), and participatory action research (PAR) methods. Language and ideas were sampled from the following sources:
• The Canadian Aboriginal AIDS Network MOU on Principles of Research Collaboration
• The Memorandum of Understanding for the Community Organizing Part of Community Action Against Asthma (Between: University of Michigan School of Public Health, Detroiters’s Working for Environmental Justice (DWEJ), the Detroit
331 Note that this MOU should not be used as a substitute for a legal document. It is not intended for this purpose; however, the principles herein may offer a useful supplement to the expectation, practice, and ethical considerations of the collaboration.
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Hispanic Development Corporation (DHDC) and Warren Conner Development Coalition (WCDC)).
• Healthy African American Families Community Participatory Research Collaboration Agreement
• Language Revitalization In Vancouver Island Salish Communities project (http://www.docstoc.com/docs/135504197/Memorandum-‐of-‐Understanding)
• Collaboration Toolkit: Creating an MOU, from Colorado Collaboration Award (http://www.growourregion.ca/images/file/Collaboration%20Toolkit%20-‐Creating%20an%20MOU.pdf)
• Indigenous Research Protection Act by Indigenous Peoples Council on Biocolonialism
• Model Tribal Research Code by the American Indian Law Center For questions, information, or to provide input, contact Max Liboiron at [email protected].
Version 02, July 2014.
This work is licensed under a Creative Commons Attribution-‐NonCommercial-‐ShareAlike 4.0 International License.
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Memorandum of Understanding
This Memorandum of Understanding made on and effective from the _________ day of __________________, ___
is created between [community group]
and [researcher or research institution/second group]
I. Background
• Describe the parties, including who is part of them (who this MOU covers) • Liaison Officials: First and Second Points of Contact for each organization and their
contact information and/or full list of participants with contact people specified (specified contact people eases communication efforts during project work)
• Describe the project II. Shared Goals and Objectives The Parties have entered into a collaborative project to work towards the following goals and objectives:
• The project seeks to enhance the community’s welfare through increasing capacity for the community to address its own issues.
• The project will be designed to increase community knowledge of the issue. • The project will be designed in ways that enhance research capacity or other
information gathering capacities of the community participants in the process. • The research objectives, questions, and/or methods must not only reflect academic
interests but strive to ensure that the research is also relevant, beneficial, and valuable to local communities.
• Community and academic participants will be involved in all project phases, including planning, implementation, research, evaluation, analysis, interpretation, and dissemination; the burden under this code is on the researcher to show that tribal, community, or individual input would be inappropriate rather than the reverse.
• All participating members (academic and community participants) are acknowledged as having expertise and commitment that is relevant to the scope of the project.
• Interested members of the community and community agencies will be provided opportunities to participate meaningfully in the research process, where the mode and scope of participation is proposed and accepted by both groups.
• Project membership is considered to be open or inclusive of those who wish to join and are willing to participate actively, rather than closed or exclusive in membership.
• Community participants and academic participants will be partnered with each other on all/certain specific tasks as a way to work together on analytic issues, including interpretation, synthesis, and verification of conclusions, gathering data and other aspects of methodology.
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• For a worksheet on “Indicators for Promoting Equitable Collaboration,” see Access Alliance, 2011.
III. Process Roles, duties, and responsibilities of each organization:
Meetings • Parties will meet a minimum of [number of times per time period]. • [The PI or project coordinator or rotating member drawn from either party] will
provide each member of the research team with notes of meetings, including decisions made, within [a reasonable time frame].
Project Design • Outline roles of each party and/or roles of individuals or groups within those
parties. • Parties will seek to combine traditional and innovative forms of research. • The project will periodically assess the experience of participating for community
and academic participants and attend to their concerns.
Data Parties should agree on what counts as data in this partnership: photographs, stories, field notes, surveys, interviews, artifacts, local knowledge, etc.
Informed Consent • The (purpose of) research project will be explained to all stakeholders
(participants and community members) in a language that is appropriate to the community. This is part of a wider community consent.
• It is requested that each participating community partner have at least one participating member (i.e., the Council representative) complete a certification of training for human subjects research through the academic partner’s institution, whether it is an Internal Review Board (IRB), journalism ethics, etc. This is not to give academic ethics priority, but to ensure that all parties are familiar with the terms and processes academics are minimally accountable for (for integrating community and institutional ethics more formally, see Khanlou and Peter 2005).
• The research team will explain potential risks and benefits in a manner that is appropriate to the community. This includes not only risks of the research to individual participants but also to the wider community and third parties (see Underkuffler 2007).
• Since researchers cannot always anticipate risks of research to the wider community, particularly if they are not familiar with the community, at least one member of the research subject population must be involved to speak to the risks of particular types of research done in that area.
• The informed consent of individual community members must be secured in writing before they participate in research or recordings, including any restrictions the individual community members might wish to attach to the
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use of this information or recordings. Written informed consent is evidenced by the signature of the individual community member on the Participant Consent Form. In cases where written forms of consent are not appropriate, another method of acknowledging consent with clear indications of when it has been obtained will decided on by both parties.
Confidentiality Statement • Unless the respondent waives confidentiality for specified uses, all researchers, both
academic and community, shall hold as privileged and confidential all information that might identify a respondent with his or her responses. We shall also not disclose or use the names of respondents for non-‐research purposes unless the respondent grants us permission to do so.
• All data will be used in a form that will make it impossible to determine the identity of the individual responses. That is, responses will not be integrated, analyzed, or reported in any way in which the confidentiality of the responses is not absolutely guaranteed.
Data Ownership Parties should discuss what it means to own, hold, or steward data and the responsibilities this entails. • Originals of all audio/visual recordings (in digital and/or analog formats) and copies
of all notes, transcripts, photographs, and other records of the research will be kept by [List parties].
• [List parties] will retain a copy of the full data file, de-‐identified appropriately. • Any site owning data, or participating in collecting data for the project, must review
its participation and role through their internal IRB and/or other indication of ethical protocols decided by group members.
• All participating sites/partners will receive a summary of the data even if their involvement is minimal and they are not entitled to the full data.
• The parties will ensure that a final, permanent repository for the research materials, to be created by the researchers, will be utilized. Additionally, the researchers will make as a condition of the deposition that the repository will provide access to community members. Further, the repository will adhere to any confidentiality or use restrictions made by the individual community members.
• Parties will outline rules for gaining or granting access to the data by third parties not listed in this MOU.
Community and Academic Validity • During the life of the project, submitted research papers and abstracts for
presentations will be circulated to all parties via lead participants at least [timeframe] and preferably [timeframe] prior to their submission for review and comment. There will be [timeframe] for comments to the lead author.
• Each project deliverable will have one or two lead individuals to permit accountability, preferably a representative from each party.
• It is expected that the first or senior author of each project will review comments from partners, discuss major differences of opinion with the partners involved, and
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circulate the final version to partners. If substantial disagreements over interpretation remain, then the lead author (first and/or senior) will include a statement in the discussion section, clarifying the nature of the disagreement.
• -‐or-‐ If there are significant disagreements over interpretation, community members can veto the publication of certain elements or all parties must reach a consensus before such elements are published. This may also be the case if some information ought to not be in the public domain according to community members or non-‐academic partners, such as but not limited to sacred knowledge.
• -‐or-‐ Team member(s) or a partner may choose to include a disclaimer if they do not agree with the content or views presented in a publication.
• Products for community release and presentation will be circulated for comments to community and academic partners, providing a [time frame] turn around time. These comments can be held in a public forum such as a community meeting, and/or in writing.
• Given that all members of the research team will be provided the opportunity to review and comment on findings prior to publication or presentation, any one member of the research team may not, particularly once initial dissemination has occurred, further analyze, publish, or present findings resulting from the above-‐mentioned research project unless the entire research team reaches a consensus.
Dissemination
• Communication strategies to present aggregate data to the community at large shall be described with in-‐progress updates where appropriate.
• Dissemination of the research results will be the responsibility of all project participants, and academic and community partners will have opportunities for presentations and publications.
• Research projects will produce, interpret, and disseminate the findings to community members in clear language respectful to the community and in ways that will be useful for developing plans that will benefit the community.
• Research shall be disseminated for public benefit, either freely (including open access) or at nominal charge to cover distribution/processing fees.
• The researchers will ensure that two copies of all publications, conference papers, and other educational and scholarly materials produced in the course of the project be deposited with the [community group, institution, etc].
• In addition to academic papers, accessible formats of research findings will be produced and distributed, such as webinars, public presentations, videos, websites, leaflets, white papers, manuals, blog posts, etc.
• All academic publications should be open access.
Publication These guidelines can be used for traditional academic publications as well as other formats for disseminating research findings.
• Due to the fundamentally collaborative nature of this partnership, party affiliations, rather than author names will be used to designate authorship of publications.
• -‐or-‐ Due to the fundamentally collaborative nature of this partnership, (1) All participants who made this research possible through conception, design, analysis,
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collection, provision or interpretation of data will be listed as an author, even when these contributions do not include writing; and (2) authors must approve the final draft and be able to defend the published work.
• -‐or-‐ Criteria outlined by Huth (1986) will be used as guidelines for authorship of publication (both academic and non-‐academic) based on the findings of the research. The criteria recommend that: (1) all authors must make a substantial contribution to the conception, design, analysis, or interpretation of data, where “substantial” is defined by parties ahead of time and updated as needed; (2) authors must be involved in writing and revising the manuscript for intellectual content; and (3) authors must approve the final draft and be able to defend the published work. Those who have made other contributions to the work (e.g. data collection without interpretation, etc.) or only parts of the above criteria should be credited in the acknowledgements, but not receive authorship.
• -‐and/or-‐ the publication contains a section outlining what each author contributed, acknowledging that “authorship” can include the collection and interpretation of information as well as actual writing up of results.
• The explicit permission of an individual or organization must be sought prior to acknowledging their contribution in a paper or presentation.
• Parties should agree on publication venues together. IV. Communication
• Include any standard or shared terminology, including consistent ways that partners are identified in written and verbal communication.
• Consider and decide on processes for reaching out to – or receiving requests from – third parties, such as the press, other groups and institutions, interested members of the public, etc.
• Consider and decide on general communications policies (social media policies, communications calendar, branding, graphic standards, etc. as applicable)
• Include any information flow practices that will help guide how data, ideas, and needs are shared between groups.
V. Resource Allocation Payment, fees, and funding Include budget, if appropriate. Note that when money exchanges hand, a contract, rather than a memorandum of understanding, is likely more appropriate. For information on when to use a binding contract vs a MOU, see: http://ctb.ku.edu/en//tablecontents/sub_section_main_1873.htm
• Both parties shall contribute in-‐ kind, including the following funding, labor, equipment, and space [list]
• [List partner] will handle all financial transactions on behalf of the collaboration. The following [reports, procedures, or financial controls] are required of [the partner]
• Expenses inclusive of [list types] will be handled by [outline procedure & responsibilities]
Also consider:
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• Gift acceptance policies: these should describe how gifts are accepted, recorded, and acknowledged. In addition, the MOU should describe the circumstances under which a gift would be declined.
• Policies around sharing fundraising information externally and among partners, and responsibility of fundraising
• Payment. Which partners or individuals will be paid and from what source? VI. Decision Making Processes
• Things to specify: • Whether the collaboration uses a consensus model, majority vote, or another system
to reach decisions. • What constitutes a full group meeting or quorum (minimum number of people
required), and what types of discussions or decisions may or may not take place without the full group/quorum.
• How partners will be informed in advance about decision-‐making discussions & what alternative voting systems may be used (voting via email, sending a proxy to a meeting, etc)
VII. Risk • The MOU should address key areas of risk for the collaboration. Partners may be
expected to maintain certain types or levels of insurance coverage, conduct background checks on employees and volunteers, maintain security of electronic data, etc.
• Since researchers cannot always anticipate risks of research to the wider community, particularly if they are not familiar with the community, at least one member of the research subject population must be involved to speak to the risks of particular types of research done in that area.
VIII. Terms of Agreement
• This agreement may be amended at any time by signature approval of the parties’ signatories or their respective designees.
• The term of this Memorandum of Understanding is from ___________________ to ___________________ and may be renewed. The Parties will review this agreement [annually/timeframe].
IX. Termination
• In case of a dispute arising from the implementation of this Memorandum of Understanding, the Parties shall exhaust alternative dispute resolution models, such as negotiation and mediation, before employing other forms of dispute resolution, such as arbitration or adjudication. Parties shall act in good faith to resolve the dispute.
• Any Party may withdraw at any time from this MOU by transmitting a signed statement to that effect to the other Parties. This MOU and the partnership created thereby will be considered terminated thirty (30) days from the date the non-‐withdrawing Party receives the notice of withdrawal from the withdrawing Party.
X. Execution and Approval
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• The persons executing this MOU on behalf of their respective entities hereby represent and warrant that they have the right, power, legal capacity, and appropriate authority to enter into this MOU on behalf of the entity for which they sign.
• Signatures _________________ • Date _________________
Version 02, July 2014. This work is licensed under a Creative Commons Attribution-‐NonCommercial-‐ShareAlike 4.0 International License.
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Works Cited and other resources Access Alliance Multicultural Health and Community Services. Community-‐Based Research Toolkit: Resources and Tools for Doing Research with Community for Social Change. Toronto: Access Alliance Multicultural Health and Community Services, 2011. http://accessalliance.ca/sites/accessalliance/files/CBR_Toolkit_Jan2012.pdf. A superb and comprehensive source of tools, templates and resources compiled and developed by the Community Based Research team at Access Alliance based on half a decade of implementing Community Based Research projects. Denzin, Norman K., and Michael D. Giardina. Ethical Futures in Qualitative Research: Decolonizing the Politics of Knowledge. Left Coast Pr, 2007. Huth, E. (1985). Guidelines on authorship of medical papers. American College of Physicians. Annals of Medicine, 104, 269-‐274. In the belief that authors and potential authors may be helped by explicit statements of justification for authorship, the following guidelines are offered for research papers, case-‐series analyses, case reports, review articles, and editorials. These guidelines are based on statements issued by the International Committee of Medical Journal Editors (ICMJE). Khanlou, Nazilla, and Elizabeth Peter. “Participatory Action Research: Considerations for Ethical Review.” Social Science & Medicine 60, no. 10 (2005): 2333–40. PAR researchers and members of Research Ethics Boards could benefit from an increased understanding of the array of ethical concerns that can arise. We discuss these concerns in light of commonly held ethical requirements for clinical research (social or scientific value, scientific validity, fair subject/participant selection, favourable risk–benefit ratio, independent review, informed consent, and respect for potential and enrolled participants) and refer to guidelines specifically developed for participatory research in health promotion. We draw from our community-‐based experiences in mental health promotion research with immigrant and culturally diverse youth to illustrate the ethical advantages and challenges of applying a PAR approach. We conclude with process suggestions for Research Ethics Boards. Maiter, Sarah, Laura Simich, Nora Jacobson, and Julie Wise. “Reciprocity An Ethic for Community-‐Based Participatory.” Action Research 6, no. 3 (September 1, 2008): 305–25. In this article we suggest that the notion of reciprocity — defined as an ongoing process of exchange with the aim of establishing and maintaining equality between parties — can provide a guide to the ethical practice of CBPAR. Through sharing our experiences with a CBPAR project focused on mental health services and supports in several cultural-‐linguistic immigrant communities in Ontario, Canada, we provide insights into our attempts at establishing reciprocal relationships with community members collaborating in the research study and discuss how these relationships contributed to ethical practice. We examine the successes and challenges with specific attention to issues of power and gain for the researched community. We begin with a discussion of the concept of reciprocity, followed by a description of how it was put into practice in our project, and, finally,
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conclude with suggestions for how an ethic of reciprocity might contribute to other CBPAR projects. Minkler, Meredith. “Ethical Challenges for the ‘outside’ Researcher in Community-‐Based Participatory Research.” Health Education & Behavior 31, no. 6 (2004): 684–97. This article explores several key challenges. These are (a) achieving a true “community-‐driven” agenda; (b) insider-‐outsider tensions; (c) real and perceived racism; (d) the limitations of “participation”; and (e) issues involving the sharing, ownership, and use of findings for action. Case studies are used in an initial exploration of these topics. Green et al.’s guidelines for appraising CBPR projects then are highlighted as an important tool for helping CBPR partners better address the challenging ethical issues often inherent in this approach.