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Women Get Pull Requests Accepted More (Except When You Know They're Women) (peerj.com)
An anonymous reader writes: In the largest study of gender bias [in programming] to date, researchers found that women tend to have their pull requests accepted at a higher rate than men, across a variety of programming languages. This, despite the finding that their pull requests are larger and less likely to serve an immediate project need. At the same time, when the gender of the women is identifiable (as opposed to hidden), their pull requests are accepted less often than men's.
Maybe women ask for pull-requests more nicely?
If it weren't for deadlines, nothing would be late.
Gender issues are a real and serious problem. And you don't need to be a "SJW" to get that. Inefficiencies introduced by biases are bad because they make less good code get written or accepted. This harms *everyone*. And understanding exactly how much of a bias there is and where there is bias or isn't bias is important. If there's no problem in a given area, then we should know about that so we can focus resources elsewhere. We don't lose by getting more good data about the situation.
What is a pull request? Is it a good or bad thing?
Is it possible that those women who don't feel it necessary to point out their gender in situations where gender doesn't matter tend to also be those more likely to communicate well?
Is it possible that those women who make it a point to draw attention to their gender in situations where there is no reason to bring up gender at all, are also more likely to be less convincing regarding the usefulness of their work?
"pull requests" heh.
Posted intentionally to lampoon typical responses.
I am not surprised that requests are not followed up on when a female calls for them, nor am I surprised that their responses are more often responded to when the gender is hidden/neutral. What I am surprised is that female pull requests are "larger and less likely to serve an immediate project need". Does this mean that female developers are concentrating on "big picture features" more often ?
If only we could fall into a woman's arms without falling into her hands
Have you tried Craigslist?
Clearly, Clarissa didn't contribute anything, and Chris may or may not have contributed anything significant, it's hard to tell.
After reading the article it appears that women lead pull acceptance in every case except for one edge case, and not by very much(its like 64% vs 63%). Nothing interesting at all here.
love is just extroverted narcissism
I honestly don't mind submissions about gender issues on /. But I do have a problem with posting articles that have not yet been peer reviewed. It is at least good of the link to make that perfectly clear.
If you killing yourself would mean I'd see the acronym "SJW" less often on Slashdot, then by all means go right ahead.
We should start dealing in those black-market beagles.
Charts that show a percentage range (ie. 60% to 80%) instead of the actual percentage (0% to 100%) to exaggerate differences between amounts on the chart.
love is just extroverted narcissism
I checked it in but my pull request was rejected :(
If I have been able to see further than others, it is because I bought a pair of binoculars.
First off they trumpet the fact that they discovered that women's merge acceptances were higher than mens. It's only when they sliced the one hundred thousands of accounts for "gender confirmation" that they decided that bias existed because success rates went from 72% to 64% - The error deviation of that alone should cover the spread.
Secondly the sample rating is awful - They compare TWO MILLION male checkins to ONE HUNDRED THOUSAND female checkins without any criteria for context, quality, need or style... just "quantity" and say that because the PERCENTAGE RATES FOR ACCEPTANCE are "higher" it must mean the women programmers are "Better" when comparing 2 sample sets with 20x the difference of checkins as they're all EQUAL.
Sorry. That's BS.
This is not science, this is propaganda statistics and poor statistics at that but I'm sure they made full use of their government funding to study gender issues in STEM fields.
It's a research study. If you have a problem with how the research was conducted or believe that the conclusions which have been drawn from the study are erroneous or the result of a particular methodological flaw feel free to point it out. Dismissing scientific results on the basis that you don't like them or people are using it for some political narrative isn't reasonable.
Also, it doesn't look like anyone here is calling for diversity quotas or any other particular action. I'm sure some people will use this to point out why company X needs some program or some such stuff, but take umbrage with them or their policy, not the scientists who made an observation.
There's no need for comparative statistics for men vs women, which leave you trying to control for all sorts of nebulous factors like how nicely they make requests, or how the genders might code differently.
All you have to do is take a bunch of coders (men or women, doesn't matter), and have them submit a bunch of code online using a male persona, using a female persona, and anonymously (or at least gender-neutral). Then compare acceptance rate for each individual. That neatly eliminates all other factors since you're comparing the same individual to himself or herself.
You're absolutely right. The fact 50% of domestic violence victims have 0% of federal funding and shelters, and 50% of rape victims aren't even legally recognized, is a real and serious problem.
Manufactured "discrimination" about pull requests is neither real nor serious.
A bullet may have your name on it but splash damage is addressed "To whom it may concern."
The magnitude of the bias reported isn't alarmingly high so some of the things you suggest and others might be reasonable to consider as origins of the difference.
However, the change of the acceptance rate histogram from uni-modal to bi-modal when the gender is known for a woman seems to be much stronger evidence of gender bias.
The bottom axis of the histogram is rate of code rejections for an individual, and the left axis is the number of individuals with that rejection rate. When gender is not known both men and women have dominantly high acceptance rates tailing off towards low accpetance rates. However when gender is know a sharp second peak at the 90% rejection rate shows up on the women's histogram but not the men.
Thus I think what this study shows is that for the most part women work on code in ways that produces code more likely to be accepted. The fact that it tends to be longer and not something on the bug list may make their submissions different (more substantial infrastructure not defect fixes might be one interpretation). So I'm not inclined to conclude much from that. But the bimodality seems to be evidence of a strong gender bias among a small number of open source projects.
Some drink at the fountain of knowledge. Others just gargle.
Yeah, at this point I pretty much just immediately flip the bozo bit on anyone who uses the term "SJW" non-ironically. It conveys no useful information except that the person using it is... um... possibly a troglodyte.
It's the ultimate ad-hominem. When you don't like what someone is saying, when it makes you uncomfortable, just call them an SJW. It signals to others that they should be modded down.
It's basically doing exactly what they accuse SJWs of, only it's fine for them because they are just cutting through the bullshit.
const int one = 65536; (Silvermoon, Texture.cs)
SJW, n: "Someone I don't like, and by the way I'm a fuckwit" - AC
Clearly, we're supposed to get mad that the research was ever even done and then stomp on it as hard as we can to make it go away.
Sometimes research results are just research results. They may not indicate any particular course of action. But lots of people will flip the fuck out anyway because they think that the facts will be used to push action in a direction they don't like.
An interesting anagram of "BANACH TARSKI" is "BANACH TARSKI BANACH TARSKI"
I just want to add it if this is the case, it may certainly affect his pull requests.
The interesting thing the study actually found was that pull-request acceptance rates dropped for BOTH males and females when the gender of the requester could be inferred from their username or avatar picture. In some categories that rate dropped more for males, and in others the rate dropped more for females.
But they ignored the drop in rates for males and considered only the drop in rates for females when jumping to their conclusion of "gender bias".
Congrats, you are not an asshole.
Play Command HQ online
The whole premise seems to be accepted pull requests = accepted developers. I mean they say:
"To what extent does gender bias exist among people who judge GitHub pull requests?
To answer this question, we approached the problem by examining whether men and women are equally likely to have their pull requests accepted on GitHub, then investigated why differences might exist."
The authors note that women are more likely to submit pull requests that aren't tied to existing open issues. They seem to conclude that this reinforces the idea that women have the best track records, that these requests are the hardest to get accepted.
"Thus, if women more often submit pull requests that address an immediate need and this is enough to improve acceptance rates, we would expect that these same requests are more often linked to issues."
I interpret that totally the other way. The paper equates getting a pull request accepted with being accepted, that's just not how (in my experience) development works. If you submit a patch for some feature add that only you've thought of, and it conflicts with nothing else, it's easy for a maintainer to accept. A patch for a known, open issue is much more likely to have regression considerations, and compete with other patches. If five people all submit a patch for one issue, odds are good at least four of them are going to be rejected. It's kind of like measuring an employee's productivity by how many lines of code they write. Experienced developers see that as largely silly.
There's a whole article that I can use to teach undergrads about how fraudulent methodology can fabricate any result you want. Their methodology is, as usual for a socjus "study", total garbage and their results are neither statistically significant nor are their conclusions based on sound logic.
A bullet may have your name on it but splash damage is addressed "To whom it may concern."
First statistical conclusion in the article is faulty: the significance is based on a chi square with df = 3,064,667. Every difference is significant with a df that high. The second statistical conclusion has the same error: significant, but the difference here is marginal. These people should really think if the underlying data truly only represents a difference in gender and all other possible variables are identical.
But a large part of the article focuses on arguments like "they feel dejected" while in reality the numbers hardly differ. Not only that, they are even in the women's favor, even on the first request. How can you then complain about feelings of dejection or abandoning because of "an unreasonably aggressive argument style" (as if women are by definition incapable of that)? No, it's just clutching at straws because they have to write an article.
But it's the final graph that is the nail in the coffin of this article: even with their self-chosen statistics, there is no difference in acceptance rate for men and women when gender is known (although "known" is too strong a word), even in the outsider category. They then phrase it like this: "There is a similar drop for men, but the effect is not as strong" while not having even the cheapest statistical argument to support it. That's the best they can come up.
So the conclusion of this article should be: women have a slight advantage in pull requests on github. The rest is FUD.
Then why are SJW's the only ones wanting to push the whole "gender 'issue'" as a "problem"?
It has not been an issue until:
They could make it an issue
They could silence all meaningful criticism
Twitter supports and protects racists - by smearing their critics with the "Hate Speech" label.