evaluation of e participation efficiency with biodiversity measures - the case of the digital agenda...

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Evaluation of E- Participation Efficiency with Biodiversity Measures - The Case of the Digital Agenda Vienna John May, Hannes Leo, Alfred Taudes CEDEM 2015

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Evaluation of E-Participation Efficiency with Biodiversity Measures - The Case of the Digital Agenda Vienna

John May, Hannes Leo, Alfred TaudesCEDEM 2015

The context

How we started working on this paper?My role in this endeavour?The content of the presentation?

Digital Agenda Vienna: The process

DAW: digitaleagenda.wien

DAW: Basic Facts

171 ideas372 participants2451 votes296 commentsIs this good or bad? How to compare this process to others?

What is e-participation

Generates a flow of information from participants to sponsorHow to measure effectiveness and efficiency of eparticipation projects?

● Effectiveness: impact on what sponsor thinks, plans, does

● Efficiency: extent of information flow from participants to sponsor

An analogy from ecology

Community and diversity: Distinction between mainland and island populations: ● Mainland population contains the full range of e-

participation topics/issues● Island population (issues) have migrated from mainland● A more efficient project will produce a greater flow of

issues

How to measure the flow?

Measure the # of issues in community = issue richness (species richness)Example: Project 1: 10 issues, 1 mentioned 91 times

Project 2: 10 issues, each mentioned 10 timeIssues richness = 10 in both cases

Flows (frequency distribution) are very different

Ecologist use diversity indices to measure such communities, e.g. Shannon entropy

Shannon entropy, effectic number of species, ENI?● Shannon entropy - 0.50, 2.30 for the above

examples - not very enlightening● Can be easily transformed into a quantity

number called “effective number of species”● John May renamed this “effective number of

issues” = 1.65, 10 = second project was about 6 times as efficient (10/1.65)

Some minor technical details...

ENI: exp(H’), H´= - ∑px ln(px)● H´=Shannon entropy ● px=relative frequency of each issue x● ln(px)=natural logarithm of relative frequency● ENI=raise the number ´e´to the power of the Shannon

entropy

Minimum=1 (single issue referendum) maximum=no upper limitEmpirical sample of 70 projects: 150 rare

And? What is the score for the DAW?

It´s so simple I replicated it for ODAP

Open-Data-Aktionsplan.de - an ongoing project done for the German Ministry of the Interior and the D21 Initiative194 participants, 254 ideas, 2574 votes, 233 comments

The score is 189,9 - A new record!

Possible extensions and research

● Can be applied to any participation/consultation/engagement method that generates a frequency distribution

● Apply to closed questions, i.e. agree/disagree, Likert scale, multiple choice…

● Build a knowledge base of e-participation projects

And finally…

Thank you for attention!You may check this out ● www.digitaleagenda.wien● opendataaktionsplan.de● discuto.io● cbased.comor get in touch: [email protected]