Although we’d all like academia to be a true meritocracy, ample research shows that implicit biases create significant hurdles to achieving diversity in our communities.
Here is an overview of the data (showing both the extent to which gender biases cause problems in science, and the different factors that may be significant contributors) and possible solutions.
What’s the problem?
- The fraction of women in academia drops off steeply throughout career ladder, also when corrected for class composition at time of graduation
- Women are paid less for the same jobs
- Women receive smaller start-ups as assistant professors. Sege et al. JAMA (2015)
- Salk Institute sued for gender discrimination
- University of Arizona sued for gender pay gap
- Harassment and bias against women in STEM – overview of references by @McLNeuro
What’s going on here? Personal stories and case studies
- Does gender matter? Ben Barres, Nature (2006)
What’s going on here? Observational studies
- Women are invited to give fewer talks at top U.S. universities20% difference after adjusting for base rate of professors. Nittrouer et al. PNAS (2018)
- Men are 15% more likely to share data with another man. Massen et al. Sci. Rep (2017)
- Women are underrepresented as reviewers, editors and last authors. Murray et al. bioRxiv (2018)
- Women are underrepresented, and cited less, in high-impact journals. Shen et al. bioRxiv (2018), Bendels et al. PLoS ONE (2018), Nature’s blog post
- In peer review, editors of both genders favour same-gender authors. Helmer et al. eLife (2017), Murray et al. bioRxiv (2018)
- Women are half as likely to receive excellent recommendation letters. Dutt et al. Nature Geoscience (2016)
- Women get less credit for the same contribution/effort on publications. Feldon et al. Soc Sci, 2017
- Women received lower grant scores than men with comparable career success. Tamblyn et al. (2018)
- Women have lower application, funding and renewal rates for NIH grants. Pohlhaus et al. Academic Medicine (2011); Kaatz et al. Academic Medicine (2016)
- Female grant applicants are equally successful when peer reviewers assess the science, but not when they assess the scientist. Witteman et al. bioRxiv (2017)
- Women and men are equally productive per year, but women drop out at higher rates. Huang et al. PNAS (2020)
- Ellemers, N. (2018). Gender Stereotypes. Annual review of psychology, 69, 275-298.
Don’t want to read a bunch of science on implicit bias? This Pixar short hits the nail on the head
What’s going on here? Randomized studies
- ‘Brian’ is hired for tenure-track psychology job 70% vs. ‘Karen’ 55% of the time. Steinpreis et al., Sex Roles (1999)
- Male students with identical CVs are judged to be more competent, hireable, deserving of mentoring and $3000 higher salary. Moss-Racusin et al. PNAS (2012)
- “Male” teaching assistants are rated better in online class. MacNell, et al. Innov Higher Ed (2015)
- Professors are less likely to informally meet women/minority students (interestingly, no advantage of contacting a professor of the same gender or race). Milkman et al. J. Appl. Psychol. (2015)
What’s going on here? Simulations
- Without changing the culture, interventions/quotas won’t close the gender gap. Momennejad et al. (2019). Computational Justice: Simulating Structural Bias and Interventions. bioRxiv, 776211.
- NYTimes simulates the cumulative effects of small acts of bias
- Thread by Michael Hendricks
- Thread by Katie Grogan
- Thread by Ken Wong
- Gender Bias in Academe: An Annotated Bibliography of Important Recent Studies
But surely I’m unbiased?
- Test your own implicit bias. https://implicit.harvard.edu
- Women’s behavior is just as biased as men’s.
- Raymond, Nature (2013)
- But… men less likely to believe research on gender bias Handley et al. PNAS (2015)
- Diverse groups are more creative. Woolley, Science (2010)
- Check the bias in your citation patterns, and add Citation Diversity Statements to your work
- cleanBib lets you get gender statistics from your paper’s reference list
- Gender Citation Balance Index tool, easy-to-use webtool to check your reference lists
- How balanced is your Twitter feed? https://www.proporti.onl/
Are there any solutions?
- Yes! This article provides an excellent and comprehensive overview of different ways to tackle gender bias. Llorens A et al. (2021) Gender bias in academia: A lifetime problem that needs solutions. Neuron 109:2047–2074.
What can I do?
- Promote, nominate, credit, suggest your women colleagues. Call out imbalanced seminar series, conferences, labs, panels, prizes, hiring pools.
- Resources: https://biaswatchneuro.com, http://www.anneslist.net, http://compcog.science, http://www.winrepo.org/, https://www.nexxt.ruhr-uni-bochum.de/
- List of lists (with databases of women/minorities in different fields) on Anna Shapiro’s lab website
- Diverse speakers in STEM: very large list of lists
- Thread by Iris van Rooij on how to be an ally for women in science
- Avoid mansplaining, manterrupting and gendered assumptions
- Do not sit on all-male panels.
- Sign the Gender Avenger pledge: https://www.genderavenger.com/the-pledge/
- Set criteria before review, aim to hire/review blindly. Uhlmann & Cohen. Psychol Sci (2005)
- Beware gendered language in evaluations and recommendation letters. Madera et al. J Appl Psychol (2009); Madera et al. (2018)
- Hold all your colleagues to the same standards: volunteering, mentoring, service tasks. Babcock et al. American Economic Review (2017)
- How female scientists can combat gender bias in the workplace
What can we do as a community?
- Guidelines for inclusive academic practice, eLife ambassadors
- Increasing gender diversity in the STEM research workforce. Greider et al., Science 366, 692–695.
- Consider ‘choice architectures’: let people opt-out, rather than opt-in, to be considered for things (promotion, talk invites, etc): He et al. 2021
- At seminars, encourage a woman to ask the first question
- This encourages the following questions to reflect the audience gender composition (Carter et al. 2018)
- Blind peer review. Budden et al. Trends in Ecology & Evolution (2008)
- Evidence-based implicit bias training
- WAGES: Workshop Activity for Gender Equity Simulation. http://wages.la.psu.edu/
Will any of this work?
- We’re in for the long haul: a total review bias of 3.7% (one point lower for one reviewer on NIH 9 point scale) translates to a 20% lower grant success rate. Day, Research Policy (2015)
- How long until your field reaches gender parity? Holman et al. PLoS Biology (2018); https://lukeholman.github.io/genderGap/
Stories and support
- Compilation on women in neuroscience by Fleur Zeldenrust
- How does she do it? Motherhood and science, stories collected by Adrienne Fairhall
- Mama is an academic blog