I guess people will be blind to information that disagrees with them. They DO discuss it, it's right there at the end:

>In every country, the men came out ahead; the gap overall was smallest in China, and biggest in the U.S.

But if you are looking at a difference of between gender (0.4) and thinking you'd rather hear them talk about that than the difference between nationality (double that at 0.8) then you clearly are coming to the conversation with a narrative already in mind and are not looking at the data.

The line you quoted isn't discussion, it is stating results. In the article and the paper there is discussion about the differences in education between countries. For example...

(From the article)

> CS departments and CS education in general have a longer history and are much more established in the United States than in China or India especially.

> A lot more is spent per student in the U.S. than in the other three countries.

(From the paper)

> United States have likely similar math and science levels as students in Russia (Implying the difference in CS scores comes from somewhere else)

But the line you quoted was the only "discussion" on the differences in gender in the article. I think there has to be at least something to discuss.Why is the gender gap smallest in China? Why is there a gap at all for people sitting in the same classes? Are there any other metrics (like age?) that had similar differences? Did the size of the gap change when looking at elite institutions?

To be completely fair, there was some discussion in the last paragraph in the paper, but the only hint at what might be causing the difference was that the women in CS aren't high achieving enough. I wouldn't count that as in depth discussion on the cause.

> The gender gap in skills does indicate that more effort is needed to attract higher-achieving female students into CS [...]

Just putting a chart at stating results the end of a paper and an article isn't a discussion. There is a gap between genders about half as large as the gap between countries. Having questions about that doesn't imply a narrative.

You only seem interested in discussing gender to bring up the ghost of genetic predispositions. But if you want to twist the study in that way, you'll have to first say that you think that whites are genetically superior to non-white in the world of computer science. After all, doesn't the study show the gap is even greater for nationality?

Just come out and say it, instead of having this proxy argument over gender.

If it isn't clear: this study can not be used to draw conclusions about racial or gender predispositions. This can only be used to show the status of education on this particular test and for people graduating right now. It doesn't control for how those people got there. If you want to twist it in some other way, that's your prerogative, but you're doing shoddy science.

You're making very strong claims against me, but I think we're on the same side. The paper disappoints me because they attempt to think of all the explanatory variables for the differences between countries, then they turn to gender and end on a chart. It's impossible to read the paper and come away with the impression that whites are simply better at coding, because the authors give so much context to their findings.

I am agnostic to the cause of the gender difference, and clearly this study didn't have enough information to give any insights. That's why I find it so unsatisfying that they would put up a chart and mention gender differences if they weren't interested or capable of expanding on the results beyond "[...] more effort is needed to attract higher-achieving female students into CS."

> You only seem interested in discussing gender to bring up the ghost of genetic predispositions. But if you want to twist the study in that way

This feels really disingenuous. The questions they brought up are perfectly fine, it's you who seems to be intent on poisoning the discussion by attacking them instead of discussing the issue.

> you'll have to first say that you think that whites are genetically superior to non-white in the world of computer science. After all, doesn't the study show the gap is even greater for nationality?

Nationality is not the same thing as race/ethnicity. At the very least, in the US, a substantial percentage of CS majors are of Asian descent, not European.

"Did Bush do 9/11? Can jetfuel really melt steel beams? Are other races and genders worse at computer science than me because they are genetically inferior? I'm just asking questions!" said the online poster.

Just because they measured the data, does not imply they were trying to explain it. To explain a result would need a very different experimental setup. Every scientist knows this. It's not a conspiracy if a scientist declines to throw darts at a wall on the cause of something. In fact, they specifically say the cause is likely not explainable by the test differences, which were too small.

"The within-country gender gaps in skills are small enough, however, that they may explain little about gender gaps in CS graduates’ labor market outcomes".

In fact, the poster is not "just asking questions". They have a clear opinion, and ignored the researcher's own statements to the contrary.

During my CS studies, a considerable number of male students were computer geeks who were playing with computers pre-uni. Female students, OTOH, were 100% oblivious of any fundamental concepts (ex. what is programming). That could explain the disparity - they had a lot of catching up to do, esp. at the beginning.

It wasn't 100% at all. If anything, I found that the only female students were the computer nerds and there were almost no "CS-curious" women enrolling.

I'd also then go on to say in my experience the student who went into a CS program for the stability and potential money "CS-curious" over passion for it might do better on scholastic tests of computer science while being less capable of actually programming well.

> This article put the gender graph at the end and moved on without discussion

The deltas between genders are quite a bit smaller than those between nationalities, though!

I mean, sure, I guess you could trigger the proverbial SJWs by pointing out the fact that a US woman is about a third of a standard deviation worse than her male classmates. But it's hard to draw firm conclusions about gender when Chinese and Indian men are at an even larger handicap relative to her.

I mean, I gotta flip this around here: why do you feel like we should be focusing on a small subsignal when the data supports a clearer, more defensible, and more hypothesis-rich conclusion about something else?

The paper disappoints me because they attempt to think of all the explanatory variables for the differences between countries, then they turn to gender and end on a chart.It's impossible to read the paper and come away with the impression that whites are simply better at coding, because the authors give so much context to their findings.

I am agnostic to the cause of the gender difference, and clearly this study didn't have enough information to give any insights. That's why I find it so unsatisfying that they would put up a chart and mention gender differences if they weren't interested or capable of expanding on the results beyond "[...] more effort is needed to attract higher-achieving female students into CS."

The data is available (although anonymized), so you can just run your own analysis.

Here's the mean z-score (and its standard deviation in brackets) aggregated by country, elite status and gender:

  China      elite   male:   0.360 (1.534)
  China      elite female:  -0.338 (1.046)
  China  non-elite   male:  -0.730 (0.623)
  China  non-elite female:  -0.791 (0.514)
  India      elite   male:   0.181 (1.304)
  India      elite female:  -0.538 (0.918)
  India  non-elite   male:  -0.847 (0.599)
  India  non-elite female:  -0.921 (0.459)
  Russia     elite   male:  -0.374 (0.975)
  Russia     elite female:  -0.829 (0.602)
  Russia non-elite   male:  -0.739 (0.594)
  Russia non-elite female:  -0.897 (0.445)
  USA        elite   male:   0.872 (1.006)
  USA        elite female:   0.563 (1.174)
  USA    non-elite   male:   0.146 (0.958)
  USA    non-elite female:  -0.248 (0.818)