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And suppose that the best people of all genders go to work at corporations, but a bigger percent of men go there than women.Then being non-gendered would be a higher sign of quality in a man than in a woman.Most of this analysis is not original to me – Hacker News had figured a lot of it out before I even woke up this morning – but I think it’ll at least be helpful to collect all the information in one easily linkable place.The study is Gender Bias In Open Source: Pull Request Acceptance Of Women Vs. It’s a pretty neat idea: “pull requests” are discrete units of contribution to an open source project which are either accepted or rejected by the community, so just check which ones are submitted by men vs.
By comparing obviously gendered participants with non-obviously gendered participants whom the researchers had nevertheless been able to find the gender of, they should be able to tell whether there’s gender bias in request acceptances.Once again, it’s hard to tell by graph-eyeballing whether these two numbers are within each other’s confidence intervals.The paper concludes that “for insiders…we see little evidence of bias…for outsiders, we see evidence of gender bias: women’s acceptance rates are 71.8% when they use gender neutral profiles, but drop to 62.5% when their gender is identifiable.It hides on page 16 that men also have fewer requests approved when their gender is known.It describes the effect for women as larger, but does not report the size of the male effects, nor whether the difference is statistically significant.
This is obviously a silly just-so story, but my point is that without knowing why all genders show a decline after unblinding, it’s premature to speculate about why their declines are of different magnitudes – and it doesn’t take much to get so small a difference. There’s no study-wide analysis, and no description of how many different subgroup analyses the study tried before settling on Insiders vs.