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Are Claims of Fairness Toward Women in the Academy “Manufactured”? The Risk of Basing Arguments on Incomplete Data

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Abstract

Claims of widespread sexism in academic science frequently appear both in the mainstream media and in prestigious science journals. Often these claims are based on an unsystematic sampling of evidence or on anecdotes, and in many cases these claims are not supported by comprehensive analyses. Here we illustrate the importance of considering the full corpus of scientific data by focusing on a recent set of claims by two philosophers of science who argue that researchers who fail to find evidence of sexism in some domains of academia–such as tenure-track hiring, grant funding, journal reviewing, letters of recommendation, and salary–are as epistemically and socially problematic as are those who deny claims of anthropogenic climate change. We argue that such claims are misguided, and the result of ignoring important evidence. We show here that when the totality of evidence is considered, claims of widespread sexism are inconsistent with the canons of science.

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Source: The College Board, 2013. (Reproduced from Ceci et al., 2014)

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Notes

  1. Others have made a similar claim. For example, Bakker and Jacobs (2016) argued that “Convergent evidence is so evocative that denying gender bias in academia would be equivalent to denying climate change.”.

  2. Multiple studies published in peer-reviewed scientific journals show that ~ 97% of actively publishing climate scientists agree that global warming over the last century is extremely likely to be the result of human activities, a conclusion endorsed by the leading scientific organizations worldwide: “The number of papers rejecting AGW [Anthropogenic, or human-caused, Global Warming] is a miniscule proportion of the published research, with… an overwhelming percentage (97.2% based on self-ratings, 97.1% based on abstract ratings) endorses the scientific consensus on AGW.” (Cook et al., 2016, p. 6) Contrast this consensus with claims that gender bias is systemic and pervasive in the tenure-track academy. The latter boasts no comparable degree of consensus nor is it based on comprehensive data treatment, rendering the comparison misguided.

  3. Witteman et al., to their credit, also list areas in which women are not disadvantaged.

  4. L&P assert that we used flawed methodology by dint of examining gender bias in tenure-track hiring among highly qualified applicants: “by selecting only top-qualified candidates to examine gender bias in hiring processes in academia (Williams and Ceci 2015b), they clearly make poor.

    methodological choices.” This criticism ignores the reality that finalists for most tenure-track positions are unambiguously excellent, something we heard repeatedly from participants in our 2015 series of experiments. However, because this was anecdotal, in Spring, 2020 we decided to test this claim more systematically. We conducted a national stratified survey of tenure-track faculty in eight fields, seven of which are STEM fields, asking faculty to rate on a 10-point scale the competence of their short-listed applicants in their most recent tenure-track search. 279 tenure-track faculty responded and their median rating was 9.0 out of 10.0 (Q3-Q1 = 2.55, indicating clustering at the right tail); 88.5% of these respondents said the top candidate in their search was between very strong and truly outstanding. Even their top three applicants were all rated, on average, as excellent. This seems to be a reality in tenure-track hiring–applicants who make it to the short list based on their CVs, letters, interviews, talks, etc. are regarded by those doing the hiring as excellent. Such unambiguous excellence is not always the case when hiring postdocs, lab managers, and student employees.

  5. A stage 1 registered replication has been attempting to replicate the Moss-Racusin et al. findings and it will be interesting to see their results. If the team—composed of both supporters and critics of the Moss-Racusin et al. findings–fails to replicate, it will undermine the claim of gender bias even at lower levels than professorial hiring, since this study is the most cited evidence of hiring bias (Ceci et al., 2023).

  6. For example, a national analysis of computer science hiring was commissioned by the Computer Research Association (Stankovic & Aspray, 2003). Women PhD-holders applied for fewer academic jobs than men (6 positions vs. 25 positions), yet they were offered twice as many interviews per application (0.77 vs. 0.37 per application). And women received 0.55 job offers per application vs. 0.19 for men: “Obviously women were much more selective in where they applied, and also much more successful in the application process” (p. 31)(http://archive.cra.org/reports/r&rfaculty.pdf).

  7. Card et al. (2022) showed that between 1960 and 1990 women had a lower chance of being inducted into the highly prestigious National Academies of Science and the American Academy of Arts and Sciences; however, this disadvantage became neutralized around 1990, and by 2000, women were 3 to 15 times more likely to be inducted into the these organizations than men with comparable publications and citations.

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  1. Department of Psychology, Cornell University, Ithaca, USA

    Stephen J. Ceci & Wendy M. Williams

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  1. Stephen J. Ceci
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Both authors contributed to the paper’s conception and writing. The first draft of the manuscript was mostly written by SJC and WMW commented and extended the argument in all versions of the manuscript. Both authors read and approved the final manuscript.

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Correspondence to Stephen J. Ceci.

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Ceci, S.J., Williams, W.M. Are Claims of Fairness Toward Women in the Academy “Manufactured”? The Risk of Basing Arguments on Incomplete Data. Sexuality & Culture 28, 1–20 (2024). https://doi.org/10.1007/s12119-023-10133-8

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