That paper that says women are better coders than men but are judged on their gender? It doesn’t say that at all
February 20, 2016
As a long-standing proponent of preprints, it bothers me that of all PeerJ’s preprints, by far the one that has had the most attention is Terrell et al. (2016)’s Gender bias in open source: Pull request acceptance of women versus men. Not helped by a misleading abstract, we’ve been getting headlines like these:
- Study: Female Coders Better Than Men, But Perceived As Worse (LiveScience)
- Women accepted as better coders as long as no gender link (TechXplore)
- Women devs – want your pull requests accepted? Just don’t tell anyone you’re a girl (The Register)
But in fact, as Kate Jeffrey points out in a comment on the preprint (emphasis added):
The study is nice but the data presentation, interpretation and discussion are very misleading. The introduction primes a clear expectation that women will be discriminated against while the data of course show the opposite. After a very large amount of data trawling, guided by a clear bias, you found a very small effect when the subjects were divided in two (insiders vs outsiders) and then in two again (gendered vs non-gendered). These manipulations (which some might call “p-hacking”) were not statistically compensated for. Furthermore, you present the fall in acceptance for women who are identified by gender, but don’t note that men who were identified also had a lower acceptance rate. In fact, the difference between men and women, which you have visually amplified by starting your y-axis at 60% (an egregious practice) is minuscule. The prominence given to this non-effect in the abstract, and the way this imposes an interpretation on the “gender bias” in your title, is therefore unwarranted.
Your most statistically significant results seem to be that […] reporting gender has a large negative effect on acceptance for all outsiders, male and female. These two main results should be in the abstract. In your abstract you really should not be making strong claims about this paper showing bias against women because it doesn’t. For the inside group it looks like the bias moderately favours women. For the outside group the biggest effect is the drop for both genders. You should hence be stating that it is difficult to understand the implications for bias in the outside group because it appears the main bias is against people with any gender vs people who are gender neutral.
Here is the key graph from the paper:
(The legends within the figure are tiny: on the Y-axes, they both read “acceptance rate”; and along the X-axis, from left to right, they read “Gender-Neutral”, “Gendered” and then again “Gender-Neutral”, “Gendered”.)
So James Best’s analysis is correct: the real finding of the study is a truly bizarre one, that disclosing your gender whatever that gender is reduces the chance of code being accepted. For “insiders” (members of the project team), the effect is slightly stronger for men; for “outsiders” it is rather stronger for women. (Note by the way that all the differences are much less than they appear, because the Y-axis runs from 60% to 90%, not 0% to 100%.)
Why didn’t the authors report this truly fascinating finding in their abstract? It’s difficult to know, but it’s hard not to at least wonder whether they felt that the story they told would get more attention than their actual findings — a feeling that has certainly been confirmed by sensationalist stories like Sexism is rampant among programmers on GitHub, researchers find (Yahoo Finance).
I can’t help but think of Alan Sokal’s conclusion on why his obviously fake paper in the physics of gender studies was accepted by Social Text: “it flattered the editors’ ideological preconceptions“. It saddens me to think that there are people out there who actively want to believe that women are discriminated against, even in areas where the data says they are not. Folks, let’s not invent bad news.
Would this study have been published in its present form?
This is the big question. As noted, I am a big fan of preprints. But I think that the misleading reporting in the gender-bias paper would not make it through peer-review — as the many critical comments on the preprint certainly suggest. Had this paper taken a conventional route to publication, with pre-publication review, then I doubt we would now be seeing the present sequence of misleading headlines in respected venues, and the flood of gleeful “see-I-told-you” tweets.
(And what do those headlines and tweets achieve? One thing I am quite sure they will not do is encourage more women to start coding and contributing to open-source projects. Quite the opposite: any women taking these headlines at face value will surely be discouraged.)
So in this case, I think the fact that the study in its present form appeared on such an official-looking venue as PeerJ Preprints has contributed to the avalanche of unfortunate reporting. I don’t quite know what to do with that observation.
What’s for sure is that no-one comes out of this as winners: not GitHub, whose reputation has been wrongly slandered; not the authors, whose reporting has been shown to be misleading; not the media outlets who have leapt uncritically on a sensational story; not the tweeters who have spread alarm and despondancy; not PeerJ Preprints, which has unwittingly lent a veneer of authority to this car-crash. And most of all, not the women who will now be discouraged from contributing to open-source projects.