Thanks :)

In fact, if you go over the whole site, even the innocuous articles ultimately reveal a terror that there might eventually be very little about the world that is specifically male, and little that is specific to males, culturally or biologically. Whereas women have wombs.

(Aside: I just noticed that The Spearhead’s subtitle is “Piercing the Shield of Ignorance”. O. M. G. HAHAHAHAHAHA. They’re complaining about women’s takeover of SF while using a metaphor and style lifted directly from bad historical bodice-rippers…)

There’s plenty to be criticized about the humanities side of things. I just wanted to point out what’s pretty much a commonplace about literature in English: that the novel was generally understood, until the early to middle twentieth century, to be a form of literature written for women, often by women, about women’s lives. So, most survey courses on literature in English will spend at least some time discussing relationships between men and women, and the personal lives and differing points of view of women and men.

My sense is that a fundamental aspect of sexism is that masculinity is defined as a discipline of ignoring anything feminine — thus, for people who wish to assert their masculinity, discussing the lives and priorities of women is an interruption of discipline. This is, I think, why some technically oriented men will boast, early and often, that they hated English classes.

It’s like explaining atoms to kids: you give them this initial model of the electrons rotating around like planets, and even though it’s not entirely accurate as a model, it provides a basis for you to later explain the electron shell if they pursue further study in physics. This presentation is meant to be the basis for understanding that the difference between the means is not large enough to explain the gender gap by itself, since that basic concept seems to elude a lot of people.

You asked specifically about the graph: I guess maybe you can’t quite read the information below the graph in the slideshare embedded version (it’s quite legible in the original, which you can download), but it’s just a graph showing the approximate magnitude of the difference in the means, not showing the full distribution. The paper itself has some fascinating numbers about ability gaps over time, and I believe if you search for it in google scholar and click on a few of the pdf links, you can eventually find an online copy that doesn’t require subscription.

There *are* papers with more accurate graphs, but I didn’t have any handy and, given that I’m trying to build simplified mental models here, it would have just added to the mental task load with very little benefit. As I said earlier, the tails represent few individuals, and wouldn’t have much effect on the final stats. And on top of that, I’m relatively sure that genius-level math folk don’t often go into computer science, so one would likely need to chop off the tail entirely… all in all, it would have added several slides and complicated the explanation to very little benefit. The key to great presentations is often knowing what to leave out.

Should be easy enough to find better graphs if you to go a university library and ask a librarian, though. Alternatively, you could check the references from one of the many books on the subject of gender and ability. And finally, an organization like http://www.ncwit.org/ might have a reading list or provide some suggestions if you asked.

The thing is, I’ve done some analysis of psychological measurement and I’ve noticed that psychologists have this tendency to treat everything as a measurement of normal means problem, up to and including making probability statements about them. And so my question is: Are those curves in your presentation population curves (ie, representing the spread and diversity of ability within the population), or are they margins of error resulting from the calculation of a sample average (a so-called “data point” that is averaged across your sample, with its associated error bars, normal as per the Central Limit Theorem when the sample size is large-ish)? It is one thing to compare the populations with your cutoff points (which are basically relative probability statements), but doing those comparisons on the confidence in average ability is much different, especially if the shapes of the curves differ. Means are influenced by outliers– they only match up with medians or modes if the population distribution is symmetric. Even if your confidence in the value of the mean is this bell-shaped distribution, depending on the shape of the population, there could be many more or fewer people extended out beyond those means. From my reading of the caption in the picture, it’s looking at an effect size, which generally is a comparison of means.

Not that I’m arguing for a biological distinction between men and women on mathematics skills, but universally, it seems like everyone except statisticians are terrible at stats.

There seems to be a conspiracy of contemporary writers who write specifically to give English teachers something to teach.

Somehow I don’t think that this reaction is particularly gendered, but maybe that’s just me. I think it does bias tech students away from this sort of thing because it seems like the point is to figure out the right BS that will please teacher.

Sarcasm aside, someone on another list I read recommended this book, which seems to talk about the magnification effect:

In one of the eye-opening studies cited in Lise Eliot’s masterful new book on gender and the brain, mothers brought their 11-month-olds to a lab so the babies could crawl down a carpeted slope. The moms pushed a button to change the slope’s angle based on what they thought their children could handle. And then the babies were tested to see how steep a slope they could navigate.

The results?

Girls and boys proved equally adept at crawling and risk-taking: On their own, they tried and conquered the same slopes. But the mothers of the girls — unlike the mothers of the boys — underestimated their daughters’ aptitude by a significant margin.

“Sex differences in the brain are sexy,” Eliot writes. And so we tend to notice them everywhere. “But there’s enormous danger,” she says, in our exaggeration. It leads us to see gender, beginning at an early age, only in terms of what we expect to see, and to assume that sex differences are innate and immutable.

From this washington post article.