why so few women in science? meg urry yale university
TRANSCRIPT
Why So Few Women in Science?
Meg Urry Yale University
More women are earning science and engineering doctorates
0
10
20
30
40
50
60
Year
Per
cent
Wom
en P
hDs
Social Sciences
Life Sciences
Physical Sciences
Engineering
Per
cen
t W
om
en P
hD
s
But higher attrition for women between B.S. and Ph.D. degrees
0%
10%
20%
30%
40%
50%
60%
70%
% w
om
en
de
gre
e r
ec
ipie
nts
Non S&EBachelor's
Non S&EPhDs
S&EBachelor's
S&E PhDs
SOURCE: NSF, Women, Minorities and Persons With Disabilities in Science and Engineering-2004
In most fields, degrees are increasingly awarded to women. Biology & medicine now ~50%.
Bachelor’s degrees in science
Attrition between B.S. and Ph.D. degrees
54% 42% All fields47% 26% Math16% 12% Physics
Women in Physics
Ivie & Ray 2005
Women in Astronomy
Ivie & Ray 2005
Career Disparities
Long 2001Sonnert & Holton 1996 Synthetic cohorts, e.g., NSF fellows –
career advancement of women slower
Salary Disparities
Egan & Bendick 1994 – factors that affect salary
Tesch et al. 1995 – resource allocation in academic medicine appointments
Reasons for Disparities?
Not family (Mason & Goulden 2002 “Do Babies Matter?”)
Xie & Shauman 2003 – interest not correlated with ability in science
Why worry?
Excellence of scienceFairness/justiceIt’s a great life!
taxpayers support science, so should benefit equally
Health of science profession more scientifically literate public more public support of science
Workforce issues …
What’s going on?
Not conscious discrimination or overt prejudice
Not differences in innate abilityKey issue: tilted playing field
Wenneras & Wold 1997 Nature Double-blind refereeing
Common Myths
Paludi & Bauer 1983, psychology paper sent to 180 referees (men & women)
John T. McKay
Joan T. McKay
J. T. McKay
Men 1.9 3.0 2.7
Women 2.3 3.0 2.6
(1=excellent, 5=bad)(1=excellent, 5=bad)
Author Author
Referee Referee
Women aren’t as good as men at science…
Women lack math ability …Stereotype threat: performing below
ability because of expectationsExample: “hard” math test
Men: 25/100 Women: 10/100 Gender gap in math ???
“This test has been designed to be gender neutral”
Women: 20/100 Men: 20/100
Also important for minorities
They prefer “caring” fields like medicine
Women don’t like physics…
Women in academic medicine are equally far behind
Hypothesis: More elite, competitive culture fewer women
There aren’t any good women to hire …
Jane DoeJohn DoeKeisha DoeJamal Doe
Women can be friendly or competent, not both
(Research shows name strongly affects success of resume, even among psychologists who are well aware of gender schemas.)
Women choose family over career…
• Women w/o children not more successful
• Many women in other demanding fields • Countries w strong support systems
(e.g., Scandinavia) have few women in physics
• Academic careers flexible: become a professor, have a family!
Biernat, Manis & Nelson 1991Porter & Geis 1981Butler & Geis 1990
Scientists are completely objective …
size matters…size matters…
blind audition…
Works for orchestras, writers, abstracts, resumes …… but not for job talks!
See story of Munich Philharmonic trombonist (Abby Conant)
Job searches are gender-blind …
Tony DeCicco, women’s soccer coachBoston Globe, June 18, 1999
Coaching (Mentoring)
If you need mentoring, you’re not good enough …
Women in Astronomy I - Baltimore, MD 1992
Women in Astronomy II – Pasadena, CA 2003
What’s going on? What’s going on? “Gender “Gender Schemas”Schemas”
Lower expectations for womenUneven evaluation
Accumulation of disadvantage
Virginia Valian Why So Slow? The Advancement of Women
Uneven Evaluation
Heilman et al. 2004 – rating asst. VPs
Norton, Vandello & Darley 2004 – rating resumes for construction job
Uhlman & Cohen 2005 – shifting criteria and (non)objectivity
Trix & Penska 2003 – letters of recommendation
Valian annotated bibliography: www.hunter.cuny.edu/genderequity/ equityMaterials/Feb2008/annobib.pdf
Sanbonmatsu, Akimoto & Gibson 1994 (Evaluation of failing students)
What’s going on? What’s going on? “Gender “Gender Schemas”Schemas”
Lower expectations for womenUneven evaluation
Accumulation of disadvantage Martell, Lane & Emrich 1996 – 1%
bias, 8 levels 65% male top management
Most of us are biasedVirginia Valian Why So Slow? The Advancement of Women
www.hunter.cuny.edu/genderequity/equityMaterials/Feb2008/annobib.pdf
Mahzarin Banaji implicit.harvard.edu
What to do?
Remedies
Women and men: educate yourselves Recognize uneven playing field Nix “lower standards”
Young women: Find the right back burner Be prepared
Leaders: lead Pressure Training (e.g., how to hire, Denton/UWa) Accountability
NAS Study: “Beyond Bias and NAS Study: “Beyond Bias and Barriers: Fulfilling the Potential of Barriers: Fulfilling the Potential of Women in Academic Science and Women in Academic Science and
Engineering”Engineering”
Statistics Learning and performance intrinsic difference? Persistence and Attrition Evaluation of success implicit bias Strategies that work Undergraduate Carnegie Mellon
Hiring faculty U. Washington toolkit Training women faculty CoaCH ADVANCE CRLT players
Institutional structures, career paths Recommendations
Change is within reach …
… but it requires action
50% women scientists unmarried
(in developed countries)Women marry
scientists/professionals