Among outsiders, women still get more requests accepted using than men. Among outsiders, revealing genders appears to show a bias against women. Its not clear if this is statistically significant. When all genders are revealed among outsiders, men appear to have their requests accepted at a rate of 64, and women. The study does not provide enough information to determine whether this is statistically significant. Eyeballing it it looks like it might be, just barely. The study describes its main finding as being that women have fewer requests approved when their gender is known. 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.
Once again, its hard to tell by graph-eyeballing whether these two numbers are within each others confidence intervals. The paper concludes that for insiderswe see little evidence of biasfor outsiders, we see evidence of gender bias: womens acceptance rates are.8 when they use gender neutral profiles, but drop.5 when their gender is identifiable. There is a similar drop for men, but the effect is not as strong. In other words, they conclude there is gender bias among outsiders because obvious-women do worse than gender-anonymized-women. They admit that obvious-men also do worse than gender-anonymized men, but they ignore this effect because its smaller. They do not report doing a test of statistical significance on whether it is really smaller or not. Among insiders, women get more requests accepted than men. Among insiders, people are biased towards women, that is, revealing genders gives women an advantage over men above homework and beyond the case where genders are hidden.
Among insiders, women do the same as men when gender is hidden, but better than men when gender is revealed. In other words, if you know somebodys a woman, youre more likely to approve her request than you would be on the merits alone. We cant quantify exactly how much this is, because the paper doesnt provide numbers, just graphs. Eyeballing the graph, it looks like being a woman gives you about a 1 advantage. I dont see any discussion of this result, even though its half the study, and as far as I can tell the more statistically significant half. Among outsiders, women do the same as/better than men when gender is hidden, and the same as/worse than men when gender is revealed. I cant be more specific than this because the study doesnt give numbers and Im trying to eyeball confidence intervals on graphs. The study itself say that women do worse than men when gender is revealed, so since the researchers presumably have access to their real numbers data, that might mean the confidence intervals dont overlap. From eyeballing the graph, it looks like the difference is 1 ie, men get their requests approved 64 of the time, and women 63 of the time.
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Its 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. Women and whether one gender gets a higher acceptance rate than the other. This is a little harder than it sounds people on GitHub use nicks that dont always give gender cues but the researchers wrote a program to automatically link contributor emails to google Plus pages so they could figure out users genders. This alone cant rule out that one gender is genuinely doing something differently than another, so they had another neat trick: they wrote another program that automatically scored accounts on obvious gender cues: for example, somebody whose nickname was JaneSmith01, or somebody who had. 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 theres gender bias in request acceptances. Because gitHub is big and their study is automated, they manage to get a really nice sample size about.5 million pull requests by men and 150,000 by women. They find that women get more (!) requests accepted than men for all of the top ten programming languages.
They check some possible confounders whether women make smaller argumentative changes (easier to get accepted) or whether their changes are more likely to serve an immediate project need (again, easier to get accepted) and in fact find the opposite womens changes are larger and less likely. That makes their better performance extra impressive. So the big question is whether this changes based on obviousness of gender. The paper doesnt give a lot of the analyses I want to see, and doesnt make its data public, so well have to go with the limited information they provide. They do not provide an analysis of the population as a whole (!) but they do give us a subgroup analysis by insider status, ie whether the person has contributed to that project before.
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