Quotulatiousness

August 14, 2016

Captured Tanks – Bagpipers I OUT OF THE TRENCHES

Filed under: Europe, History, Military, WW1 — Tags: , , , , — Nicholas @ 03:00

Published on 13 Aug 2016

Indy sits in the chair of wisdom again to answer your questions about World War 1.

When virtue signalling became the dominant form of social media content

Filed under: Media, Politics, Quotations — Tags: , , , — Nicholas @ 02:00

Dan Sanchez explains why political “discussions” on social media tend to be worse than useless:

When children are free to learn from undirected experiences, they learn to conceive of truth as something that guides the successful pursuit of their own goals. But in the domineering, tightly-directed environments of school and the modern household, we condition our children to conceive of truth as received wisdom handed down by authority.

Children are largely deprived of the noble joy of discovering truths as revealed by successful action. Instead they are left with the ignoble gratification of pleasing a taskmaster by reciting an answer that is marked “correct.” And this goes far beyond academics. For the modern child, learning “good behavior” is not about discovering through trial and error what kinds of behaviors are conducive to thriving socially. Instead, it’s about winning praise and avoiding censure from authority figures.

Thanks to this conditioning, we have all become approval-junkies, always on the lookout for our next fix of external validation: for the next little rush of dopamine we get whenever we are patted on the head by others for being a “good boy” or a “good girl,” for exhibiting the right behavior, for giving the right answer, for expressing the right opinion.

This is why the mania for virtue signalling is so ubiquitous, and why orthodoxies are so impervious. Expressing political opinions is not about hammering out useful truths through the crucible of debate, but about signaling one’s own virtue by “tattling” on others for being unvirtuous: for being crypto-commies or crypto-fascists; for being closet racists or race-traitor “cucks;” for being enemies of the poor or apologists for criminals.

Much of our political debate consists of our abused inner children basically calling out, “Teacher, teacher, look at me. I followed the rules, but Johnny didn’t. Johnny is a bad boy, and he said a mean word, too. Teacher look what Trump said. He should say sorry. Teacher look what Hillary did. You should give her detention.”

You can’t expect much enlightenment to emerge from this level of discourse.

QotD: Women in graduate math programs

Filed under: Quotations, Science, USA — Tags: , , — Nicholas @ 01:00

Academic programs presumably want people with high ability. The GRE bills itself as an ability test, and under our expanded definition of ability this is a reasonable claim. So let’s talk about what would happen if programs selected based solely on ability as measured by GREs.

This is, of course, not the whole story. Programs also use a lot of other things like grades, interviews, and publications. But these are all correlated with GRE scores, and anyway it’s nice to have a single number to work with. So for now let’s suppose colleges accept applicants based entirely on GRE scores and see what happens. The STEM subjects we’re looking at here are presumably most interested in GRE Quantitative, so once again we’ll focus on that.

Mathematics unsurprisingly has the highest required GRE Quantitative score. Suppose that the GRE score of the average Mathematics student – 162.0 – represents the average level that Mathematics departments are aiming for – ie you must be this smart to enter.

The average man gets 154.3 ± 8.6 on GRE Quantitative. The average woman gets 149.4 ± 8.1. So the threshold for Mathematics admission is 7.7 points ahead of the average male test-taker, or 0.9 male standard deviation units. This same threshold is 12.6 points ahead of the average female test-taker, or 1.55 female standard deviation units.

GRE scores are designed to follow a normal distribution, so we can plug all of this into our handy-dandy normal distribution calculator and find that 19% of men and 6% of women taking the GRE meet the score threshold to get into graduate level Mathematics. 191,394 men and 244,712 women took the GRE last year, so there will be about 36,400 men and 14,700 women who pass the score bar and qualify for graduate level mathematics. That means the pool of people who can do graduate Mathematics is 29% female. And when we look at the actual gender balance in graduate Mathematics, it’s also 29% female.

Vast rivers of ink have been spilled upon the question of why so few women are in graduate Mathematics programs. Are interviewers misogynist? Are graduate students denied work-life balance? Do stereotypes cause professors to “punish” women who don’t live up to their sexist expectations? Is there a culture of sexual harassment among mathematicians?

But if you assume that Mathematics departments are selecting applicants based on the thing they double-dog swear they are selecting applicants based on, there is literally nothing left to be explained.

I am sort of cheating here. The exact perfect prediction in Mathematics is a coincidence. And I can’t extend this methodology rigorously to any other subject because I would need a much more complicated model where people of a given score level are taken out of the pool as they choose the highest-score-requiring discipline, leaving fewer high-score people available for the low-score-requiring ones. Without this more complicated task, at best I can set a maximum expected gender imbalance, then eyeball whether the observed deviation from that maximum is more or less than expected. Doing such eyeballing, there are slightly fewer women in graduate Physics and Computer Science than expected and slightly more women in graduate Economics than expected.

But on the whole, the prediction is very good. That it is not perfect means there is still some room to talk about differences in stereotypes and work-life balance and so on creating moderate deviations from the predicted ratio in a few areas like computer science. But this is arguing over the scraps of variance left over, after differences in mathematical ability have devoured their share.

Scott Alexander, “Perceptions of Required Ability Act As A Proxy For Actual Required Ability In Explaining The Gender Gap”, Slate Star Codex, 2015-01-24.

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