Men’s and Women’s Brains Appear to Age Differently
nymag.com
nymag.com
It reminds me of this https://en.wikipedia.org/wiki/Lysenkoism
He was completely right with his information. The bell curve for male IQ is much shallower resulting in men greatly outnumbering women both at the top and the bottom of the IQ chart (note: that doesn't mean that a man with the same IQ as a woman is smarter, just that there are more men with higher IQs).
The idea that abstract logic, spatial reasoning, and pattern recognition (what IQ indicates) are primary components in being good at STEM is well-known too. The one idea definitely implies the other and stating that causal implication shouldn't be a crime.
Nobody stepped forward to disprove the science. They simply screamed sexism. They didn't care that he had even said that fighting the sexist component was also important. They didn't care that he had said the science might be disproven. They only cared that they didn't like the idea no matter if it was true or not.
History is filled with ideologues hurting and killing men of science for ideas we now know to be true. Freedom to research, study, and publish those studies without being threatened or harmed is paramount. If there's a problem, then fight the science -- not the scientist.
1. I think it's important also to recognize that the "science" he cited was not fool-proof. In the way that most science is not (for example, check out the 50 year long & still on-going dispute with Phillip Morris about the harmful effects of tobacco). And that moreover, the specific reports he cites can certainly be disputed on the methodology (where are minority women? where are women of different socio economic classes? where are women working in technical fields whose performance do not correspond to standardized test scores? etc)
2. Even if Summers was absolutely right about sex difference between men and women, some of the outrage directed towards him came not from particular concern with his science, but the effects that such statements would have on the construction of a liberal democratic society (which Harvard, despite it's many follies, is mostly invested in). A society which operates on the truth that women just can't do math as well is one which condones expectations that adversely effect female participation in communities of scientists, mathematicians, engineers, and tech-related fields.
*edit for formatting
For example you assume the existence of some people who believe that a liberal democratic society requires women in science and tech, even if they are inherently less able in these fields. Why then, can't these people explain to the public why this is the case, so that the public will then accept the need for women in these fields, regardless of the truth regarding IQ?
summers is still a faculty member at harvard, he's just no longer president, and no longer speaking for the institution
If you forbid Larry Summers from forming one plausible opinion based on the facts, even though you allow others (e.g. the opposite opinion), then your reasoning still must be along the lines that I outlined.
Why can't the public be trusted with the fact that Larry Summers draws one opinion from the data, even though other people have drawn different opinions.
Whenever your reasoning contains something like (b), my critique is going to apply, because you are arguing against a viewpoint based on its consequences, not on its truth (even if you also doubt its truth).
The idea of relative distribution across races is a much different (and extremely dubious) question and far removed from the question of relative distribution between genders. It doesn't matter if minority women differ relative to other women if the relative difference between minority women and minority men of a given group is the same as the relative difference among the rest of the population.
The question of "other types of intelligence" is not at all related. The jobs in question are STEM. Something like social intelligence is very important, but does not make someone good at STEM and is therefore irrelevant.
2) Firstly, HE DID NOT SAY THAT! Equal chance to try doesn't mean equal chance to succeed. I had equal opportunity to try out for the football team, but there's no way I had equal chance to succeed.
Secondly, if the research is true, then all women should be given the opportunity to see if they can do the job (and those that can should definitely pursue that career if they want), but society shouldn't be blamed if because there are more men than women in STEM if the science says that gender parity in STEM won't happen.
Your "liberal democratic society" idea can go to hell because incorporating the truth into society is more important than anything when it comes to moving humanity toward a better future. If current society can't deal with that truth, then it needs to be modified so human progress can continue.
http://www.ams.org/notices/200810/fea-gallian.pdf http://www.ams.org/notices/201201/rtx120100010p.pdf
They do not address the question of IQ in any meaningful way. They only seek to show that women can succeed in mathematics. Summers in no way said that women could not succeed in math (or any other STEM field). The question of IQ has more to do with potential candidates rather than success of those candidates.
If only we could all be so persecuted.
The other scientists sacrificed on the alter of ideology are seldom in a similar position. When they lose their jobs, they don't have the same money and friends to fall back onto.
A fear of retribution hurts science and hurts human progress.
Actually, the older I get the more I think they should do away with IQ tests. Or, produce one that doesn't look like it was written by a bunch of white college professors.
The RAVEN IQ test [1] cannot possibly be gender or race biased, and this is the gold standard that is used to compare people across languages and cultures.
That said, the study was done by a team in Hungary.
What this means, in practice, is that the selection criteria will be vague, which in turn is likely to produce massively varying results from study to study.
I see why you're interested in this, but I think you're basing your hypotheses on a false premise: namely that gender theory is hard science.
What exactly are you looking for?
You're expressing interest in a potential study, which is nice, but my point is that the study you're proposing is infeasible because it hinges on a non-scientific concept.
When I say that it's epistemologically difficult to find evidence for gender/sex orthogonality, I don't mean that it's a difficult study. I mean that it's difficult (if not impossible) to empirically observe gender (in the sense of "gender theory"), thus precluding any meaningful study from taking place.
In other words: we could do the measurements you're propose, but then we have no way of knowing what it is we're actually measuring. Gender, as defined by gender theory, is inscrutable.
So again, what are you interested in discovering? What do you see as the possible outcomes of your proposed study and what conclusions would you draw from those results? Maybe there's a better way of answering those questions, and maybe these questions are better suited for a different academic field (e.g. sociology).
Until they look at nonbinary and genderless people, we won't know whether sex or experience of gender is more significant in brain development. Even throwing in gender nonconforming people might produce some interesting findings.
Then I rest my case: this can't be done in any meaningful way.
To give you an intuition as to why, how do we control that our nonbinary group actually corresponds to the gender theoretical definition of nonbinary?
One could argue that this problem can be tackled by assuming two things:
1. people are being truthful when we ask them to categorize their gender
2. all nonbinary participants are categorizing themselves as such as a result of the same underlying process.
Again, the claim "my gender is of X" is unfalsifiable, and thus un-scientific.
To summarize, you can certainly produce effects, but those effects won't meaningfully contribute to any understanding of sex/gender experience.
More to the point: statistical power matters, which is only indirectly related to sample size. I mean no disrespect, but this kind of criticism usually betrays ignorance of statistics and scientific methodology.
Do you suspect the study is under-powered? If so, why?
The problem I see with it being a small group is not necessarily that the results are inaccurate because of size/lack of statistical power, but more so because of the fact that a lot of variables will not show up in a small sample. For example, it is possible that certain diseases that they found to be more common in one sex than the other are only more common in caucasians and the same relationship does not occur with other races (since this happened in Hungary, it is very unlikely that non-white people were a part of this study). The numbers determined here probably apply very well to people in Hungary but they might be very different from what we'd find in the United States or in India, for example.
This may well be an epiphenomenon, but that's why reproductibility (not statistical significance) is the gold-standard of science.
Huh? If there aren't just 2 kinds of brain, but instead 30 different kinds, then a study with 300 brains is much more likely to catch all 30 kinds of brains than a study with just 50 brains.
This is -- again -- only indirectly related to sample size. If the sample was randomly selected and the statistical power is great enough, then any correlation it's missing is marginal; this is a feature, not a bug!
You're not entirely wrong insofar as brain-imagery studies rarely conduct random samples, but increasing your sample size won't fix that.