It’s Halloween and they’re back! Williams and Ceci again

Feminist Philosophers

Recent  research reports significant faculty bias against women students in science.  

However, Williams and Ceci have an op-ed piece in the NY Times stating a conflicting conclusion from their recent research:  there’s no bias aainst women in math-intensive fields in STEM.  Their piece links to a forthcoming article by them.

Are they right?  If you have the time, you might try to analyze their work.  I don’t have the time, so let me simply urge a lot of caution when you read about the recent work.  They published a similar conclusion in 2011, and there turned out to be serious problems with their reasoning.  We discuss some of them here.

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5 thoughts on “It’s Halloween and they’re back! Williams and Ceci again

  1. I don’t work in the sciences, but I see one glaring problem with their interpretation of results. They argue that women get (roughly) the same pay, citations, publications, etc. as men and then use that as proof that there is no bias against women. This interpretation rests on the assumption that women in the sciences are not more talented and hard working than the men. Normally I would assume equal talent and motivation between the genders, but given the huge societal disincentives for women to study math or science, I don’t think it’s crazy to think that the only women who actually push through to remain in these fields are perhaps of the super-star variety.

    More specifically, since we already have studies that demonstrate that faculty members in the sciences rate a cv from a woman significantly lower than the exact same cv from a man, it seems as though, to get into a job in the first place, these women would have had to outperform their male peers fairly significantly. So, if there were no bias once they got into the institution, they would be making more money, earning tenure at higher rates, publishing and being cited more, etc. etc.

    Others have also pointed out that the study’s authors use “roughly the same” to gloss over statistically significant differences.


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