Intersex and Sex Denialism

This was a pleasant surprise.

For generations those who, for biological reasons, don’t fit the usual male/female categories have faced violence and stigma in Kenya. Intersex people – as they are commonly known in Kenya – were traditionally seen as a bad omen bringing a curse upon their family and neighbours. Most were kept in hiding and many were killed at birth. But now a new generation of home-grown activists and medical experts are helping intersex people to come out into the open. They’re rejecting the old idea that intersex people must be assigned a gender in infancy and stick to it and are calling on the government to instead grant them legal recognition.

While some of those people are trans*, that podcast does talk with a number of intersex people as well. It’s great to see more advocacy, I just wish I’d see more of it in North America and less of this.

The facts of the world generally don’t support transphobic arguments, and transphobes don’t really have the option of making robust arguments based on an honest assessment of the current state of our knowledge. They know this – they make use of this same technique of pondering counterfactuals. The difference is that they work backwards to fabricate an entirely new counter-reality, tailored to support their positions and vast enough that it can substitute for reality itself in a person’s mind. It’s called denialism: an entire ideological support system made to preserve a desired belief by rejecting the overwhelming evidence that would threaten this belief.

Denialism is wrongness with an infrastructure – ignorance with an armored shell, a whole fake world weaponized against the real world.

Less of “denialism,” that is, not good analysis or Zinnia Jones. She gets a bit meta behind the link, and the contents are applicable to much more than transphobia. It’s worth a full read.
(That last item comes courtesy of Shiv. Support her work, too!)

Intelligence and Race, in sub-populations

I’ve read a fair number of papers covering race and genes. In fact, before I go farther, here’s a bibliography:

In this article, the authors argue that the overwhelming portion of the literature on intelligence, race, and genetics is based on folk taxonomies rather than scientific analysis. They suggest that because theorists of intelligence disagree as to what it is, any consideration of its relationships to other constructs must be tentative at best. They further argue that race is a social construction with no scientific definition. Thus, studies of the relationship between race and other constructs may serve social ends but cannot serve scientific ends. No gene has yet been conclusively linked to intelligence, so attempts to provide a compelling genetic link of race to intelligence are not feasible at this time. The authors also show that heritability, a behavior-genetic concept, is inadequate in regard to providing such a link.

Sternberg, Robert J., Elena L. Grigorenko, and Kenneth K. Kidd. “Intelligence, race, and genetics.” American Psychologist 60.1 (2005): 46.

The literature on candidate gene associations is full of reports that have not stood up to rigorous replication. This is the case both for straightforward main effects and for candidate gene-by-environment interactions (Duncan and Keller 2011). As a result, the psychiatric and behavior genetics literature has become confusing and it now seems likely that many of the published findings of the last decade are wrong or misleading and have not contributed to real advances in knowledge. The reasons for this are complex, but include the likelihood that effect sizes of individual polymorphisms are small, that studies have therefore been underpowered, and that multiple hypotheses and methods of analysis have been explored; these conditions will result in an unacceptably high proportion of false findings (Ioannidis 2005).

Hewitt, John K. “Editorial Policy on Candidate Gene Association and Candidate Gene-by-Environment Interaction Studies of Complex Traits.” Behavior Genetics 42, no. 1 (January 1, 2012): 1–2. doi:10.1007/s10519-011-9504-z.

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Where Bigotry Thrives

All of us strive to be rational. We believe that reality does not contradict itself, that something cannot exist and not exist at the same time. So when we encounter a contradiction we believe in, we discard it to align ourselves closer to reality. But there’s another, more human reason to weed out contradictions in our views.

Charles Murray, in his interview with Sam Harris, was grilled a bit on universal basic income.

[1:53:17] HARRIS: I’ve heard you talk about it and this is a surprise because, in “Coming Apart” you are fairly critical of the welfare state in all its guises and you- you just said something that at least implied disparagement of the welfare state in Europe, as we know it, so tell me why you are an advocate for universal basic income.

[1:53:40] MURRAY: Well, I first wrote [a] book back in two thousand five or six, called “In Our Hands,” but I did it initially for the same reason that Milton Friedman was in favor of a negative income tax, the idea is that you replace the current system with the universal basic income and, that, you leave people alone to make their decisions about how to use it.

And yet, back in 1984, Murray was singing a different tune.

In Losing Ground, Charles Murray shows that the great proliferation of social programs and policies of the mid-’60s made it profitable for the poor to behave in the short term in ways that were destructive in the long term.

Murray comprehensively documents and analyzes the disturbing course of Great Society social programs. Challenging popular notions that Great Society programs marked the beginning of improvement in the situation of the poor, Murray shows substantial declines in poverty prior to 1964-but slower growth, no growth, and retreat from progress as public assistance programs skyrocketed.

If we truly want to improve the lot of the poor, Murray declares, we should look to equality of opportunity and to education and eliminate the transfer programs that benefit neither recipient nor donor.

Murray was influential in Reagan’s war on the poor, which argued poor people would unwisely spend their government assistance cheques, yet now he’s arguing that the poor should be given government assistance without strings attached?! He never acknowledges his about-face, but I think this part of the interview is telling.

[2:00:11] MURRAY: There will be work disincentives, but we are already at a point, Sam, where something more than 20 percent of working-age males with high school diplomas, and no more [education than that], are out of the labor force. So we already have a whole lot of guys, sitting at home, in front of a TV set or a gameboy, probably stoned on meth, or- or opioids, doing nothing. We got a problem already and I see a lot of ways in which the moral agency that an income would give could make the problem less.

[2:00:46] HARRIS: Did the dysfunction you, you see in white and largely rural America now, is it analogous to the dysfunction that we were seeing in the in the black inner-city starting a few decades ago? Are there important differences, or- or how do you how do you view that?

[2:01:05] MURRAY: In some ways it followed pretty much the same trajectory. Way back in nineteen ninety two, or three it was, I had an op-ed in the Wall Street Journal called “Becoming a White Underclass,” and I was simply tracking the growth in a non-marital births among white working-class people, and I said to myself, along with Pat Moynihan who said it better and first, that if you have communities in which large numbers of young men are growing to adulthood without a male figure, you asked for and get chaos. And I assume that what had happened in the black community when non-marital births, uh, kept on going up is going to happen in the white community. So in that sense they follow pretty much a predictable trajectory.

In the 1980’s, the face of poverty was black and addicted to drugs. Now, it’s white and addicted to drugs. Changing the race of those impoverished may have changed Murray’s views of poverty.

We dug into a contradiction Murray held, and found bigotry hiding underneath. This is no coincidence, persistent contradictions in your worldview are fertile ground for bigotry. All the atheists in the crowd know this.

To evade the charge of bigotry, you need to do more than say that you sincerely believe that the Bible is against gay marriage. You need to explain why you take the clobber verses as something important and relevant to today, while the statements like “Let the man with two tunics share with him who has none,” aren’t.

There are arguments against taking the missional verses and the poverty verses and trying them to apply them today. Of course, many of those arguments could be turned against the clobber verses as well. Can it be shown that there is a consistent means of interpretation that would lead to the clobber verses being taken literally while the charity verses should be basically ignored?

Or think of it this way: would the hypothetical “man from Mars” who was innocent of Christianity and the culture wars really look at the Bible and come away saying, “Wow, we’ve really got to do something to stop gay marriage”?

Think about how this looks from the outside. The parts of the Bible that you believe apply today are the ones that require other people to make sacrifices. The parts of the Bible that would require YOU to make big sacrifices are not considered relevant. Look at it this way, and you’ll see why “bigot” is one of the nicer things you could be called.

Contradictions allow you to pick and choose which rules you follow, allowing you to benefit while others fall into harm. It also provides a great shield against criticism.

[59:06] MURRAY: Dick and I, our- our crime in the book was to have a single, solitary paragraph that said – after talking about the patterns that I’m about to describe – “if we’ve convinced you that either the environmental or the genetic explanation has won out to the exclusion of the other, we haven’t done a good enough job presenting the evidence for one side of the other. It seems to us highly likely that both genes and the environment have something to do with racial differences.” And we went no farther than that. There is an asymmetry between saying “probably genes have some involvement” and the assertion that it’s entirely environmental and that’s what the, that’s the assertion that is being made. If you’re going to be upset at “The Bell Curve,” you are obligated to defend the proposition that the black/white difference in IQ scores is 100% environmental, and that’s a very tough measure.

Hit Murray with the charge that he’s promoting genetic determinism, and he’ll point to that paragraph in “The Bell Curve” and say you’re straw-personing his views. Argue that intelligence is primarily driven by environment and he’ll either point to the hundreds of pages and dozens of charts that he says demonstrates a genetic link that’s much stronger than environment, or he’ll equivocate between “primarily driven by environment” and “100% environmental.” Nor is this an isolated incident. Remember his bit about “large numbers of young men are growing to adulthood without a male figure, you asked for and get chaos?”

[40:23] MURRAY: … the thing about the non-shared environment is it’s not susceptible to systematic manipulation. It’s … idiosyncratic. It’s non-systematic … there are no obvious ways that you can deal with the non-shared environment, in the way that you could say “Oh, we can improve the schools, we can teach better parenting practices, we can provide more money for – …” [those] all fall into the category of manipulating the shared environment and when it comes to personality, as you just indicated, it’s 50/50 [for genes and environment] but almost all that 50 is non-shared.

[41:02] HARRIS: Yeah, which seems to leave parents impressively off the hook for … how their kids turn out.

[41:10] MURRAY: Although it is true that parents – and I’m a father of four – uh, we resist that. … and with the non-shared environment and the small role left for parenting, I will say it flat out: I read [the research of Judith Rich Harris] with *the* most skeptical possible eye. I was looking for holes in it, assiduously. …

[41:57] MURRAY: … the book was very sound, it was very rigorously done, and … at this point I don’t know of anybody who’s familiar with literature, who thinks there’s that much of a role left of the kind of parents thought they had in shaping their children.

[42:15] HARRIS: Right, well I’m not gonna stop trying, I think, it’s [a] very hard illusion to cut through… as I read Harry Potter tonight to my eldest daughter.

[42:23] MURRAY: … You know that, but I think that it’s good to reflect on that: reading Harry Potter to your eldest daughter is a good in itself.

[42:32] HARRIS: Yeah.

[42:35] MURRAY: And the fact that she behaves differently 20 years from now is not the point.

[42:38] HARRIS: No, exactly, and it is an intrinsic good, and it’s for my own pleasure that I do it largely at this point.

Murray also thinks that nothing a parent will do will change their child’s development. His ability to flip between both sides of a contradiction is Olympic.

[43:12] HARRIS: That’s the one thing that it just occurred to me people should also understand is that, in addition to the fact that IQ doesn’t explain everything about a person’s success in life and … their intellectual abilities, the fact that a trait is genetically transmitted in individuals does not mean that all the differences between groups, or really even any of the differences between groups in that trait, are also genetic in origin, right?

[43:41] MURRAY: Critically important, critically important point.

[43:42] HARRIS: Yeah, so the jury can still be out on this topic, and we’ll talk about that, but to give a clear example: so if you have a population of people that is being systematically malnourished – now they might have genes to be as tall as the Dutch, but they won’t be because they’re not getting enough nourishment. And, in the case that they don’t become as tall as the Dutch, it will be entirely due to their environment and yet we know that height is among the most heritable things we’ve got – it’s also like 60 to 80 percent predicted by a person’s genes.

[44:15] MURRAY: Right. Uh, the comparison we use in the book … is that, you take a handful of genetically identical seed-corn, and divide it into two parts, and plant one of those parts in Iowa and the other part in the Mojave Desert, you’re going to get way different results. Has nothing whatsoever to do with the genetic content of the corn.

It’s no wonder that when Harris asks him if anything discovered since publication has changed his claims, his response was no. As he inhabits both side of a contradiction, nothing could falsify his views.

Contradictions are also a way to change your views without acknowledging you did. Consider this small bit of trivia Murray throws out (emphasis mine):

[1:40:53] HARRIS: If my life depended on it, I could not find another person [besides Christopher Hitchens] who smoked cigarettes in my contact list, you know, and let’s say there’s a thousand people in there, right?

[1:41:04] MURRAY:  Hmm mm-hmm.

[1:41:05] HARRIS: That’s an amazing fact in a society where something like 30% of people smoke cigarettes.

[1:41:12] MURRAY: That’s a wonderful illustration of how isolated [we are within our classes]… because, in my case, I do know people who smoke cigarettes but that’s only because I go play poker at Charleston West Virginia casino and there, about 30% of the guys I played poker with smoked. But that’s ok. In terms of [the] American Enterprise Institute, where I work, [I] don’t know anybody who smokes there, I don’t… social circles, no.

If you had a long memory, that small tidbit packs quite a punch.

Let’s begin by referring to the basic objectives of the program:

  1. To show that the basic social cost changes are bad economics.
  2. To illustrate how smoking benefits society and its members.
  3. To show that anti-smoking groups, who are promoting the social cost issue, have self-serving ends, and are not representative of the general society.

In short, we took as our goals a defense which would undermine the concepts of the social cost issue, and an offense which would stress the social benefits of smoking and freedom to smoke.

In 1980, the American Enterprise Institute was preparing reports and training videos that argued smoking is a net benefit to society. Among other things, worker productivity was better when people took regular smoke breaks, and restrictions on cigarettes harm personal liberty.

In 2017, the number of smokers at the American Enterprise Institute is far less than in the general population. If you value being free of contradictions, a reversal like this should cause you some tough introspection about who you allow into your think-tank. If you don’t, no introspection is necessary. There’s no need to criticize yourself, no need to submit yourself to annoying audits, you can just carry on being awesome.

Like Sam Harris. Emphasis mine.

[1:39] HARRIS: Human intelligence itself is a taboo topic; people don’t want to hear that intelligence is a real thing, and that some people have more of it than others. They don’t want to hear that IQ tests really measure it. They don’t want to hear that differences in IQ matter because they’re highly predictive of differential success in life, and not just for things like educational attainment and wealth, but for things like out-of-wedlock birth and mortality. People don’t want to hear that a person’s intelligence is in large measure due to his or her genes, and there seems to be very little we can do environmentally to increase a person’s intelligence, even in childhood. It’s not that the environment doesn’t matter, but genes appear to be 50 to 80 percent of the story. People don’t want to hear this, and they certainly don’t want to hear that average IQ differs across races and ethnic groups. Now, for better or worse, these are all facts.

[5:32] HARRIS: Whatever the difference in average IQ is across groups, you know nothing about a person’s intelligence on the basis of his or her skin color. That is just a fact. There is much more variance among individuals in any racial group than there is between groups.

If the mean IQs of people grouped by skin colour are different, then you must know something about a person’s intelligence by knowing their skin colour. Head over to R Psychologist’s illustration of Cohen’s d and keep a close eye on the “probability of superiority.” For instance, when d = 0.1, the fine print tells me “there is a 53 % chance that a person picked at random from the treatment group will have a higher score than a person picked at random from the control group (probability of superiority),” which means that if I encounter someone from group A I can state they have a higher intelligence than someone from group B with odds slightly better than chance. There’s only one situation where knowing someone’s skin colour tells me nothing about their intelligence, and that’s when the mean IQs of both groups are equal.

You could counter “so what, that 53% chance is so small as to be no different than 50/50,” and I’d agree with you. But if Murray demonstrated group differences of the same magnitude, his conclusion should not have been “IQ differs between races,” it should have been “IQ is effectively equal across racial lines.” By taking this counter, you’ve abandoned the ability to say mean IQ varies across groups. “Average IQ differs across races” and “skin colour conveys information about IQ” are equivalent statements, so Sam Harris is contradicting himself.

Contradictions are a chronic problem for him. It should come as no surprise that Sam Harris is always right, and that entire websites are wrong.

A few of the subjects I explore in my work have inspired an unusual amount of controversy. Some of this results from real differences of opinion or honest confusion, but much of it is due to the fact that certain of my detractors deliberately misrepresent my views. The purpose of this article is to address the most consequential of these distortions. […]

Whenever I respond to unscrupulous attacks on my work, I inevitably hear from hundreds of smart, supportive readers who say that I needn’t have bothered. In fact, many write to say that any response is counterproductive, because it only draws more attention to the original attack and sullies me by association. These readers think that I should be above caring about, or even noticing, treatment of this kind. Perhaps. I actually do take this line, sometimes for months or years, if for no other reason than that it allows me to get on with more interesting work. But there are now whole websites—Salon, The Guardian, Alternet, etc.—that seem to have made it a policy to maliciously distort my views.

Disagreement is due to misunderstanding, not genuine error. Ergo, he cannot be a bigot.

This, then, is a strong second reason to examine yourself for contradictions. Don’t just do it to stay in line with reality, do it to help rid yourself of bigotry against your fellow person.

Gimmie that Old-Time Breeding

Full disclosure: I think Evolutionary Psychology is a pseudo-science. This isn’t because the field endorses a flawed methodology (relative to the norm in other sciences), nor because they come to conclusions I’m uncomfortable with. No, the entire field is based on flawed or even false assumptions; it doesn’t matter how good your construction techniques are, if your foundation is a banana cream pie your building won’t be sturdy.

But maybe I’m wrong. Maybe EvoPsych researchers are correct when they say every other branch of social science is founded on falsehoods. So let’s give one of their papers a fair shake.

Ellis, Lee, et al. “The Future of Secularism: a Biologically Informed Theory Supplemented with Cross-Cultural Evidence.” Evolutionary Psychological Science: 1-19. [Read more…]

Everything Is Significant!

Back in 1939, Joseph Berkson made a bold statement.

I believe that an observant statistician who has had any considerable experience with applying the chi-square test repeatedly will agree with my statement that, as a matter of observation, when the numbers in the data are quite large, the P’s tend to come out small. Having observed this, and on reflection, I make the following dogmatic statement, referring for illustration to the normal curve: “If the normal curve is fitted to a body of data representing any real observations whatever of quantities in the physical world, then if the number of observations is extremely large—for instance, on an order of 200,000—the chi-square P will be small beyond any usual limit of significance.”

This dogmatic statement is made on the basis of an extrapolation of the observation referred to and can also be defended as a prediction from a priori considerations. For we may assume that it is practically certain that any series of real observations does not actually follow a normal curve with absolute exactitude in all respects, and no matter how small the discrepancy between the normal curve and the true curve of observations, the chi-square P will be small if the sample has a sufficiently large number of observations in it.

Berkson, Joseph. “Some Difficulties of Interpretation Encountered in the Application of the Chi-Square Test.” Journal of the American Statistical Association 33, no. 203 (1938): 526–536.
His prediction would be vindicated two decades later.

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Stop Assessing Science

I completely agree with PZ, in part because I’ve heard the same tune before.

The results indicate that the investigators contributing to Volume 61 of the Journal of Abnormal and Social Psychology had, on the average, a relatively (or even absolutely) poor chance of rejecting their major null hypotheses, unless the effect they sought was large. This surprising (and discouraging) finding needs some further consideration to be seen in full perspective.

First, it may be noted that with few exceptions, the 70 studies did have significant results. This may then suggest that perhaps the definitions of size of effect were too severe, or perhaps, accepting the definitions, one might seek to conclude that the investigators were operating under circumstances wherein the effects were actually large, hence their success. Perhaps, then, research in the abnormal-social area is not as “weak” as the above results suggest. But this argument rests on the implicit assumption that the research which is published is representative of the research undertaken in this area. It seems obvious that investigators are less likely to submit for publication unsuccessful than successful research, to say nothing of a similar editorial bias in accepting research for publication.

Statistical power is defined as the odds of failing to reject a false null hypothesis. The larger the study size, the greater the statistical power. Thus if your study has a poor chance of answering the question it is tasked with, it is too small.

Suppose we hold fixed the theoretically calculable incidence of Type I errors. … Holding this 5% significance level fixed (which, as a form of scientific strategy, means leaning over backward not to conclude that a relationship exists when there isn’t one, or when there is a relationship in the wrong direction), we can decrease the probability of Type II errors by improving our experiment in certain respects. There are three general ways in which the frequency of Type II errors can be decreased (for fixed Type I error-rate), namely, (a) by improving the logical structure of the experiment, (b) by improving experimental techniques such as the control of extraneous variables which contribute to intragroup variation (and hence appear in the denominator of the significance test), and (c) by increasing the size of the sample. … We select a logical design and choose a sample size such that it can be said in advance that if one is interested in a true difference provided it is at least of a specified magnitude (i.e., if it is smaller than this we are content to miss the opportunity of finding it), the probability is high (say, 80%) that we will successfully refute the null hypothesis.

If low statistical power was just due to a few bad apples, it would be rare. Instead, as the first quote implies, it’s quite common. That study found that for studies with small effect sizes, where Cohen’s d was roughly 0.25, their average statistical power was an abysmal 18%. For medium-effect sizes, where d is roughly 0.5, that number is still less than half. Since those two ranges cover the majority of social science effect sizes, that means the typical study has very low power and thus a small sample size. Instead, the problem of low power must be systemic to how science is carried out.

In this fashion a zealous and clever investigator can slowly wend his way through a tenuous nomological network, performing a long series of related experiments which appear to the uncritical reader as a fine example of “an integrated research program,” without ever once refuting or corroborating so much as a single strand of the network. Some of the more horrible examples of this process would require the combined analytic and reconstructive efforts of Carnap, Hempel, and Popper to unscramble the logical relationships of theories and hypotheses to evidence. Meanwhile our eager-beaver researcher, undismayed by logic-of-science considerations and relying blissfully on the “exactitude” of modern statistical hypothesis-testing, has produced a long publication list and been promoted to a full professorship. In terms of his contribution to the enduring body of psychological knowledge, he has done hardly anything. His true position is that of a potent-but-sterile intellectual rake, who leaves in his merry path a long train of ravished maidens but no viable scientific offspring.

I know, it’s a bit confusing that I haven’t clarified who I’m quoting. That first paragraph comes from this study:

Cohen, Jacob. “The Statistical Power of Abnormal-Social Psychological Research: A Review.” The Journal of Abnormal and Social Psychology 65, no. 3 (1962): 145.

While the second and third are from this:

Meehl, Paul E. “Theory-Testing in Psychology and Physics: A Methodological Paradox.” Philosophy of Science 34, no. 2 (1967): 103–115.

That’s right, scientists have been complaining about small sample sizes for over 50 years. Fanelli et. al. [2017] might provide greater detail and evidence than previous authors did, but the basic conclusion has remained the same. Nor are these two studies lone wolves in the darkness; I wrote about a meta-analysis of 16 different power-level studies between Cohen’s and now, all of which agree with Cohen’s.

If your assessments have been consistently telling you the same thing for decades, maybe it’s time to stop assessing. Maybe it’s time to start acting on those assessments, instead. PZ is already doing that, thankfully…

More data! This is also helpful information for my undergraduate labs, since I’m currently in the process of cracking the whip over my genetics students and telling them to count more flies. Only a thousand? Count more. MORE!

… but this is a chronic, systemic issue within science. We need more.

Double-Dipping Datasets

I wrote this comment down on a mental Post-It note:

nathanieltagg @10:
… So, here’s the big one: WHY is it wrong to use the same dataset to look for different ideas? (Maybe it’s OK if you don’t throw out many null results along the way?)

It followed this post by Myers.

He described it as a failed study with null results. There’s nothing wrong with that; it happens. What I would think would be appropriate next would be to step back, redesign the experiment to correct flaws (if you thought it had some; if it didn’t, you simply have a negative result and that’s what you ought to report), and repeat the experiment (again, if you thought there was something to your hypothesis).

That’s not what he did.

He gave his student the same old data from the same set of observations and asked her to rework the analyses to get a statistically significant result of some sort. This is deplorable. It is unacceptable. It means this visiting student was not doing something I would call research — she was assigned the job of p-hacking.

And both the comment and the post have been clawing away at me for a few weeks, when I’ve been unable to answer. So let’s fix that: is it always bad to re-analyze a dataset? If not, then when and how?

[Read more…]

Zvan on the Gendered Pay Gap

I have a really nice document about the gendered pay gap buried on a hard drive. To write it, I spent a good few months reading policy documents and research study after research study after research study after research stu– well, you get the point. My favorite of the bunch is this one. The gender breakdown of an industry tends to vary with time, so Emily Murphy and Daniel Oesch looked into whether or not that effected pay.

Both baseline models suggest that moving from a male to a female occupation – or staying within an occupation that feminizes – entails a sizeable wage loss. Adding controls for the workplace (M1) and general human capital (M2) makes no difference: the wage penalty associated with FEM amounts to about 15 per cent for British women, British men and Swiss women, 15 and to about 5 per cent for German women, German men and Swiss men.
If women rush to your occupation, your wages drop… even if you’re a man or a childless woman. This is tough to explain as anything but discrimination.
While I’ve been mulling over how and when to release my document, Stephanie Zvan independently came up with her own version.
Let’s start by noting that at least one person who studies the factors that account for pay gaps says that choice of careers, while a factor in unequal pay, is not the silver-bullet solution that pay–gap critics suggest. It isn’t even the biggest factor driving the difference between men’s and women’s wages. […]
… even though women work fewer paid hours than men, they work the same number of hours overall. The reason women more frequently require constrained work weeks and more flexibility in their schedules is that they do the bulk of the unpaid work that makes our society run, particularly caregiving, both for children and for other adults.
It may not have an excessive number of footnotes, but her version states much the same thing as mine in fewer words and clearer language. Give it a read, in honour of International Why-Isn’t-There-An-International-Men’s-Day Day.

BBC’s “Transgender Kids, Who Knows Best?” p4: Dirty Sexy Brains

This series on BBC’s “Transgender Kids: Who Knows Best?” is co-authored by HJ Hornbeck and Siobhan O’Leary. It attempts to fact-check and explore the many claims of the documentary concerning gender variant youth. You can follow the rest of the series here:

  1. Part One: You got Autism in my Gender Dysphoria!
  2. Part Two: Say it with me now…
  3. Part Three: My old friend, eighty percent
  4. Part Four: Dirty Sexy Brains

In North America, one of our pet obsessions is dividing everything up according to sex. Gendered toys, gendered clothes, gendered bathrooms, even gendered jobs. And yet if you follow those links, you’ll find these divisions were always in flux: gender-neutral toys used to be common yet are increasingly rare; dresses were gender-neutral, and colours weren’t gendered until roughly World War I; there were no public women’s washrooms in the US until the 1880’s, because women weren’t allowed in public; and computer science flipped from being women’s work to men’s work in the span of a few decades, leading to increased salaries and prestige.

This extends all the way down to our organs.

[Read more…]