Are women evaluated fairly in academic science? A search for gender bias across six domains
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Citation (published version)Shulamit Kahn, Stephen Ceci, Wendy Williams. "Are Women Evaluated Fairly in Academic Science? A Search for Gender Bias Across Six Domains."
We attempt to resolve the vast, contradictory literature on bias against women in academic science. Claims of sexism span a wide range of evaluation contexts; over 17,000 academic articles on these topics have been published since 2013, with massively-inconsistent results. This has spurred debate within academic science, leading to sweeping claims not representative of the full research corpus. We review the empirical evidence of bias in each of six evaluation contexts: (a) tenure-track hiring, (b) grant funding, (c) teaching ratings, (d) journal acceptances, (e) salary, and (f) recommendation letters. We also explore the gender gap in productivity. We highlight analyses addressing causal factors, focusing on studies with sufficient power to make reliable generalizations. We conclude that bias may be a factor in at most two of the six contexts, teaching ratings and salary. However, uncertainties and mitigating factors in these two domains preclude greater confidence. In the remaining four contexts (tenure-track hiring, grant funding, journal acceptances, and recommendation letters) we find no compelling evidence of gender bias. We suggest that several sources of cognitive bias underpin claims of sexism in academic science, resulting in minimization of the most robust forms of evidence, reliance on findings indicating bias, and exclusion of counterevidence from the largest and most powerful studies. Given the substantial level and range of resources directed toward reducing sexism in academic science, it is essential to develop a clear understanding of precisely where such efforts are justified, to ensure that they address empiricallydocumented, real-world barriers.