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The paper doesn’t give a lot of the analyses I want to see, and doesn’t make its data public, so we’ll have to go with the limited information they provide.
They do not provide an analysis of the population as a whole (!
Another day, another study purporting to find that Tech Is Sexist.
Since it’s showing up here, you probably already guessed how this is going to end.
Because Git Hub is big and their study is automated, they manage to get a really nice sample size – about 2.5 million pull requests by men and 150,000 by women. ) requests accepted than men for all of the top ten programming languages.
They check some possible confounders – whether women make smaller 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 – women’s changes are larger and less likely to serve project needs.
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.
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.That makes their better performance extra impressive.So the big question is whether this changes based on obviousness of gender.women and whether one gender gets a higher acceptance rate than the other.
This is a little harder than it sounds – people on Git Hub use nicks that don’t 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.
) but they do give us a subgroup analysis by “insider status”, ie whether the person has contributed to that project before.