Pseudoscience is effective. If it weren’t, people wouldn’t generate so much of it to try to justify opinions not supported by the bulk of the evidence. It’s effective because people trust science as a method of understanding the world, and ideological actors want that trust conferred to their opinions. They want their opinions to carry that authority, so they imitate science to try to steal some of that legitimacy for themselves. However, science is not flattered by this behavior, it is undermined and diminished.
The Damore Manifesto (PDF with hyperlinks) or “Google anti-diversity memo” is just such an example of pseudoscience, and largely by accident, it has gained outsize attention for what is essentially a C-grade highschool research paper. We will get to a deconstruction of Damore’s scientific case for gender differences in a moment (See the Scientific Critique section below), but I would first like to point out that it has served as an excellent bellwether for those who have more sexism than sense when it comes to evaluating scientific claims. It has proven itself compelling to a large number of people in the media, for example intellectual lightweight David Brooks, who finds it so compelling he calls on Google’s CEO to resign. He makes the astonishing claim that Damore is championing “scientific research” while his opponents are merely concerned with “Gender equality” (Classic false bifurcation fallacy). He also declares Evolutionary Psychology to be “winning the debate” and goes on to talk about superior female “brain connectivity”, and with a sigh, I wonder what Snapple cap he learned these “facts” from. Not only is this highly debatable, but even if male vs female patterning exists there is no reason to think that it is unaffected by environment and cultural patterning on brain plasticity. If boys supposedly have more developed motor cortex and girls more emotional wiring is that because the boy’s first toy was a ball, and the girl’s is a doll? The declarations that this is a settled question is absurd. We don’t know, and there are too many confounders to be making statements about biological inevitability with regards to gender when we are positively soaking in gendered norms of behavior.
Brooks conclusion, an example of being incompetent and unaware of it, is the Google leadership either “is unprepared to understand the research (unlikely), is not capable of handling complex data flows (a bad trait in a C.E.O.) or was simply too afraid to stand up to a mob”. He never considers the possibility, and given this is Brooks the inevitability, that he is wrong and has been hoodwinked by rather mediocre pseudoscientific argumentation. In these reactions, we learn more about these authors’ biases than we have learned about the suitability of women to write code, as the “manifesto” conforms to Brooks’ rather predictable biases and therefore receives almost no skepticism relative to the weight of the claims, which are hefty. Why is Brooks so blind to the shoddy scholarship of the Google memo?
Ironically, within the memo itself, we have the answer:
We all have biases and use motivated reasoning to dismiss ideas that run counter to our internal values.
With this we see the continuing evolution of pseudoscience, as they continue to evolve and mimic actual scientific debate and knowledge, the scientific language of motivated reasoning (the cultural or identity-protective cognition responsible for denialism), has filtered into their lingo. This is fascinating in itself, as the author has clearly read about motivated reasoning, yet is completely blind to it for the rest of his essay. This essay is classic pseudoscience, built on motivated reasoning, that uses a half a dozen references, cherry-picked from the literature, to make the astonishing claim that women are underrepresented in his white-collar workforce because of fundamental biological differences (read defects) affecting their capability to perform in a purely intellectual job. It is another in a long line of “just so” pseudoscientific justifications of gender or racial disparities that just happens to defend the status quo (subtext – “why I shouldn’t have to sit through any more mandatory diversity training”).
This is a wonderful example of Panglossian reasoning and if you haven’t read Candide, here is an example:
Master Pangloss taught the metaphysico-theologo-cosmolonigology. He could prove to admiration that there is no effect without a cause; and, that in this best of all possible worlds, the Baron’s castle was the most magnificent of all castles, and My Lady the best of all possible baronesses.
“It is demonstrable,” said he, “that things cannot be otherwise than as they are; for as all things have been created for some end, they must necessarily be created for the best end. Observe, for instance, the nose is formed for spectacles, therefore we wear spectacles. The legs are visibly designed for stockings, accordingly we wear stockings. Stones were made to be hewn and to construct castles, therefore My Lord has a magnificent castle; for the greatest baron in the province ought to be the best lodged. Swine were intended to be eaten, therefore we eat pork all the year round: and they, who assert that everything is right, do not express themselves correctly; they should say that everything is best.”
Candide listened attentively and believed implicitly, for he thought Miss Cunegund excessively handsome, though he never had the courage to tell her so. He concluded that next to the happiness of being Baron of Thunder-ten-tronckh, the next was that of being Miss Cunegund, the next that of seeing her every day, and the last that of hearing the doctrine of Master Pangloss, the greatest philosopher of the whole province, and consequently of the whole world.
Everything old is new again. What Voltaire was mocking were the glib and facile justifications of injustice in his time, which presume the current state of the world is in its best possible state and everything you see is the result of natural inevitability. Candide in Silicon Valley would exclaim, “Oh Pangloss, why is it that men are so over-represented in tech?” and Pangloss’s response, “For men are better at tech because of their intrinsic personality traits, and in this best of all possible worlds, male personality traits and even their flaws make for the best-possible technology and business practices.”
Anyone who has been following the Uber saga might question Panglossian reasoning about why tech is male. Even if the tech sector, as it exists today, is male-dominated because men perform better in the current pathological and Machiavellian environment, that doesn’t mean this is ideal, that it isn’t hugely, culturally flawed, and maybe desperately in need of womanly empathy. Taking such data at face value, an industry that is blind to the needs of fully half of its customers, or blind to the potential benefit of the perspective of the other half of the population, is playing with fire. Do we really think situations like Uber’s are a coincidence given the toxic masculinity of its leadership? The male-dominated model is not the best of all possible worlds. The male-dominated model was built by men, for men, so why be surprised when less women are attracted to it and fare worse within it?
A Scientific Critique of Damore’s Claims
Other authors have already done some of the heavy lifting, tackling the low scientific credibility of these claims and placing them in the historical context of the usual power-dynamic of trying to scientifically justify the status quo. These are useful, but we can expand upon them and use this essay as a learning opportunity for how to detect pseudoscience, so one hopefully doesn’t have to go through all the effort of endless debunking every time an ideologue vomits up some new dreck to explain why it’s only natural males, or whites, or whomever comes out on top.
And that is one thing we should immediately detect, the similarity to historical “just-so” arguments of scientific racism from the last few centuries. These arguments are old news, as anyone who has read Stephen Jay Gould’s Mismeasure of Man can tell you, and crop up whenever the dominant class in society has to explain why they’re on top without admitting it’s because they pushed everyone else down then pulled the ladder up after themselves. Once you hear people talking about why current race or gender divisions are natural, one should immediately take whatever argument is coming with a massive dose of skepticism. We have heard this nonsense before.
Let’s start with Damore’s words so it’s clear I’m addressing the scientific claims of his argument, contained in the last element of his TL;DR section and supported by the handful of actual scientific citation.
Differences in distributions of traits between men and women may in part explain why we don’t have 50% representation of women in tech and leadership. Discrimination to reach equal representation is unfair, divisive, and bad for business.
Now keep in mind, this is in the context of an 69:31 M:F ratio at Google which is even higher in the engineering at 80:20, and arguments there is a strong business case for diversity.
Possible non-bias causes of the gender gap in tech
On average, men and women biologically differ in many ways. These differences aren’t just
socially constructed because:
● They’re universal across human cultures
● They often have clear biological causes and links to prenatal testosterone
● Biological males that were castrated at birth and raised as females often still identify
and act like males
● The underlying traits are highly heritable
● They’re exactly what we would predict from an evolutionary psychology perspective
Note, I’m not saying that all men differ from all women in the following ways or that these
differences are “just.” I’m simply stating that the distribution of preferences and abilities of men
and women differ in part due to biological causes and that these differences may explain why
we don’t see equal representation of women in tech and leadership. Many of these differences
are small and there’s significant overlap between men and women, so you can’t say anything
about an individual given these population level distributions.
It’s so nice that he cleared that up about not applying these findings to individuals this is hard to reconcile with the fact he is suggesting the 69:31 ratio or 80:20 engineering ratio at Google is in some meaningful way affected by these differences. Further, each of these statements lacks citation and can not be taken at face value, and I would describe them as either all wrong or grossly oversimplified. While the differences in gendered personality he subsequently describes is consistent within any culture examined, they are not consistent between cultures, which shows these are still culturally-dependent and not purely biologically deterministic (And of course, there is no matriarchal culture for comparison 😉 ) I have no idea why he conflated the research on androgens on personality development using CAH or androgen insensitivity with studies of personality changes in castration related to sex-reassignment, and prostate cancer treatment (if anyone can find a study of those “castrated at birth” please show me as I cant find it – I suspect he’s confused). He mixes two effects by saying androgens in the womb have effects on subsequent personality (likely but difficult to separate from gender norms) but then saying traits are heritable. Which is it? The Y chromosome or exposure to androgens? One is genetic, one is congenital. Finally, it’s rare to find examples where EP is truly “predicting” anything and not just indulging in the just-so stories and adaptationism (my favorite example of an evo-psych just so), i.e. more Panglossian logic. The field is…problematic, and strong statements about EP predictions like “exactly what we would predict from an evolutionary psychology perspective” should set off alarm bells.
Each of these statements are gross simplifications of large bodies of research, some of which are highly problematic areas with reproducibility problems, to justify a 2:1 or even 4:1 difference in hiring of men:women at Google. There is a general rule that “extraordinary claims require extraordinary proof”, well here is a man saying the reason Google has 2-4x as many men as women isn’t just the known, historic, institutional sexism that kept women from voting, owning property, having access to college education, equal pay etc., but fundamental biological differences across all cultures, that exists from birth, programmed by testosterone yet highly heritable (wah?) and conforming to predictions of a controversial scientific field that starts with conclusions and works backward to explanation. These effects are large enough, apparently, that Google should not try for parity in hiring and stop diversity training. Riiight. You better have some rock solid data to back this up.
Let’s look at the extraordinary data on why the women are so terribly disadvantaged based on their biology for software engineering (heads up, it’s a couple of wikipedia articles, and about 3 scientific citations)
Personality differences
Women, on average, have more:
Openness directed towards feelings and aesthetics rather than ideas. Women generally also have a stronger interest in people rather than things, relative to men (also interpreted as empathizing vs. systemizing).
These two differences in part explain why women relatively prefer jobs in social or artistic areas. More men may like coding because it requires systemizing and even within SWEs, comparatively more women work on front end, which deals with both people and aesthetics.
Extraversion expressed as gregariousness rather than assertiveness. Also, higher agreeableness.
This leads to women generally having a harder time negotiating salary, asking for raises, speaking up, and leading. Note that these are just average differences and there’s overlap between men and women, but this is seen solely as a women’s issue. This leads to exclusory programs like Stretch and swaths of men without support.
Neuroticism (higher anxiety, lower stress tolerance).This may contribute to the higher levels of anxiety women report on Googlegeist and to the lower number of women in high stress jobs.
Note that contrary to what a social constructionist would argue, research suggests that “greater nation-level gender equality leads to psychological dissimilarity in men’s and women’s personality traits.” Because as “society becomes more prosperous and more egalitarian, innate dispositional differences between men and women have more space to develop and the gap that exists between men and women in their personality becomes wider.” We need to stop assuming that gender gaps imply sexism.
For this segment he cites the wikipedia page on “sex differences in psychology; personality traits”, only useful for some background, not proof women!=engineers.
He cites This paper, which summarizes meta-analyses in the literature of personality with a reproducible effect showing that in a 6 dimensional model of personality traits women and men consistently score differently on being interested in “persons” vs “things”, and also that these sex differences in behavior are consistent across cultures. To be fair supporting literature exists which correlates these personality trends with differences in vocational choices, so it’s plausible that, all things being equal, there may be a gender gap in some professions based on personality traits.
This may be the only item of interest in his entire paper, as it is reproducible and there is evidence it impacts what choices the different sexes make about jobs. The problem I have with it is there is no way to control for the effect of how humans, starting when we’re toddlers, start to consolidate gender roles. If the image of the engineer or tech industry is predominantly male, this becomes a self-fulfilling prophecy. It also assumes that the current male-dominated status of tech couldn’t benefit from traits on the female axis including better interest in “persons” and creativity/artistic expression. The argument becomes a tautology, men are attracted to the tech sector because the tech sector is male. Add to that the tendency of institutions to maintain homogeneity by effects like in-group bias, and you see why male-dominated fields may remain static. Just imagine if we had accepted similar Panglossian logic 50 years ago that these gender-distributions as some kind of inevitable consequence of natural gender preferences, we’d still have only male doctors, lawyers, and executives, because, this is the best of all possible worlds, and there must be some evolutionary psychology to explain why there are no women doctors, or lawyers, or executives.
Damore then cites the wikipedia article on the Empathizing–systemizing theory. This appears to be moderately central to his argument, but again it is weak evidence. Not to beat a dead horse, but we are once again starting with the assumption that the current state of affairs represents some kind of ideal – the dominance of men in the field is “just so” because they’re more adapted to it, rather than they adapted the field to themselves or that there’s a host of historical factors such as women only got the right to vote in the last 100 years, co-ed schools in the last 50 years, they are still treated as second-class citizens including when it comes to pay. It also accepts one of the authors underlying assumptions, which is outside of my experience, which is that empathy is bad for engineering at Google. I can’t debate that, but least one former Googler has responded to this assertion and says absolutely not:
What I am is an engineer, and I was rather surprised that anyone has managed to make it this far without understanding some very basic points about what the job is. The manifesto talks about making “software engineering more people-oriented with pair programming and more collaboration” but that this is fundamentally limited by “how people-oriented certain roles and Google can be;” and even more surprisingly, it has an entire section titled “de-emphasize empathy,” as one of the proposed solutions.
People who haven’t done engineering, or people who have done just the basics, sometimes think that what engineering looks like is sitting at your computer and hyper-optimizing an inner loop, or cleaning up a class API. We’ve all done this kind of thing, and for many of us (including me) it’s tremendous fun. And when you’re at the novice stages of engineering, this is the large bulk of your work: something straightforward and bounded which can be done right or wrong, and where you can hone your basic skills.
But it’s not a coincidence that job titles at Google switch from numbers to words at a certain point. That’s precisely the point at which you have, in a way, completed your first apprenticeship: you can operate independently without close supervision. And this is the point where you start doing real engineering.
…
And once you’ve understood the system, and worked out what has to be built, do you retreat to a cave and start writing code? If you’re a hobbyist, yes. If you’re a professional, especially one working on systems that can use terms like “planet-scale” and “carrier-class” without the slightest exaggeration, then you’ll quickly find that the large bulk of your job is about coordinating and cooperating with other groups.
…
Essentially, engineering is all about cooperation, collaboration, and empathy for both your colleagues and your customers. If someone told you that engineering was a field where you could get away with not dealing with people or feelings, then I’m very sorry to tell you that you have been lied to. Solitary work is something that only happens at the most junior levels, and even then it’s only possible because someone senior to you — most likely your manager — has been putting in long hours to build up the social structures in your group that let you focus on code.
All of these traits which the manifesto described as “female” are the core traits which make someone successful at engineering. Anyone can learn how to write code; hell, by the time someone reaches L7 or so, it’s expected that they have an essentially complete mastery of technique. The truly hard parts about this job are knowing which code to write, building the clear plan of what has to be done in order to achieve which goal, and building the consensus required to make that happen.
Take a look at a recent paper from the theorist behind the E-S scale – Simon Baron-Cohen – and the differences on his Autism Spectrum Quotient scores (a newer scale Baron-Cohen has validated from the EQ SQ research and seems to have moved onto) for women vs men and STEM fields vs others that Damore is alluding to (I have to make some leaps here, Damore links the “E-S scale” wikipedia, which is a touch dated, without indicating a specific study, and ostensibly is referring to this work by Baron-Cohen who has advanced the idea of the “male mind” and autism being an excess of male mental traits – this itself has been critiqued as “neurosexist”). Studying an enormous database Baron Cohen finds a statistically-significant difference in AQ score between men and women, and women and those in STEM:
While this may be statistically significant, it’s still a tiny difference – a matter of about 3 points on this scale between men and women, and women and STEM workers who, on average, also tend to have a similar 2-3 point higher AQ score than the female mean. To put this in perspective, this is a 50 point scale, and the nonclinical range of AQ is consistently in the teens to twenties while those diagnosed with autistic spectrum disorder have a mean around 35. It is also hard to conclude the differences between women’s score and STEM isn’t due to intrinsic or cultural factors – again, the best of all possible worlds fallacy, and it is no evidence to believe that 2-3 points difference in the mean score explains 2-4 fold gaps in hiring of men vs women. Draw a line at about 21 and ballpark an SD, of +/- 8 points, are there 2-4x as many men under the curve right there? Of course not. There’s too much meat under that curve to justify more than a couple of points difference in outcomes, assuming the effect is highly meaningful or beneficial. Alternatively, you could make an argument from the tails, that you could conceive of the extremes of the population such as AQ > 40 having approximately 2x as many men with this trait. One would have to believe that the population at google is so far shifted to the right in terms of male braininess, that the majority of the population at google has a mean AQ beyond 40, basically suggesting they all would score higher than the mean for those with autism spectrum disorder.
At the same time that Damore is critical of reducing populations to their means when there is significant overlap, to believe his argument – that tech is segregated by gender because not enough women have the “male mind” described by Baron-Cohen – requires one to believe that the status-quo ratio represents the ideal workforce, that these tiny differences in gender behavior are so debilitating as to explain the 2-4x difference in hiring, and that nothing beneficial is brought to the table by “empathic” team members. This makes no sense, these differences are slight. The area under the curve doesn’t support that these tiny differences – even if they were intensely meaningful, could generate such large differences in hiring. The areas where the variance between the populations becomes larger than the female population size is far above typical scores for ASD. Is the contention that the neurotypical can’t code?
Barely worth mentioning, he alludes to negative female personality traits by including a link to this wikipedia article on Neuroticism. This is a similarly weak argument. Again the effect is meaningless in size, if you go to the primary literature it’s consistent but small. There is no evidence such an mild difference in gender behaviors with regards to neuroticism would result in such a dramatic difference in hiring or performance, nor is it explained why neuroticism would be less adaptive in engineering vs other fields.
Finally he cites this opinion piece dismissing wage gaps between genders from a Libertarian online magazine, ignored without comment.
Does anyone maybe feel already the evidence here is a bit…light? You’re going to tell an entire gender they can’t do engineering based on a 3 psychology papers showing small and likely irrelevant differences in gendered behavior, a couple of wikipedia pages, and a libertarian opinion piece about how the wage gap is imaginary? You are surprised when women read this and they’re pissed? Do those saying this is “science” like David Brooks want to maybe rethink their expertise on this topic? Because they’re not looking too competent right now. This is classic pseudoscience – a weak, cherry-picked literature is flogged to support extreme ideological nonsense.
Next Damore asks why might men be more suited for software engineering? Well he’s got a whole paragraph and three more “sciencey” citations to justify that:
Men’s higher drive for status
We always ask why we don’t see women in top leadership positions, but we never ask why we
see so many men in these jobs. These positions often require long, stressful hours that may not
be worth it if you want a balanced and fulfilling life.
Status is the primary metric that men are judged on4, pushing many men into these higher
paying, less satisfying jobs for the status that they entail. Note, the same forces that lead men
into high pay/high stress jobs in tech and leadership cause men to take undesirable and
dangerous jobs like coal mining, garbage collection, and firefighting, and suffer 93% of
work-related deaths.
To justify this he cites the Atlantic opinion piece “The War Against Boys” which counter-intuitively suggests women are better at school than boys, and it’s boys whose performance is undermined (and this helps Damore’s argument how?). He cites this paper on gender differences in mate selection criteria, sadly is paywalled but it’s conclusions are college men prefer good looks, and college women want financial success in a mate, therefore men are more competitive for status jobs in order to satisfy female sexual selection. One could point out, this is a gross simplification of human mating dynamics and is one effect among many in human attraction or every woman alive would coo over Donald Trump. Finally he cites this paper on effects of testosterone on college age men that found when injected with additional testosterone in an Ultimatum game they behaved more aggressively, but also more generous to those who made them bigger offers thus supporting the idea testosterone enhances “status seeking” behavior. Again one would have to believe this is a large enough effect that women and men have no interest in tech or engineering for any other reason than mate selection. Or show that those engineers seeking status are running higher testosterone levels than men in other “high status” jobs to show this is anything other than a suggestive result. It is further discredited by the fact that over the last 40 years women have pursued more and more “high status” jobs. Although their numbers are more uneven with regards to “things important” type (read engineering) fields, to say this is biological determinism and not male obstructionism is not justified based on a single testosterone experiment done in college students and a oversimplified view of mate selection. It ignores that women are perfectly capable of being engineers and functioning at the top of fields like physics or mathematics, and human mating behaviors are far more complex than “women are gold-diggers.”
Again. Does anyone here find the evidence here a bit light? David Schmitt seems to agree and his research is that being cited by Damore:
Still, it is not clear to me how such sex differences are relevant to the Google workplace. And even if sex differences in negative emotionality were relevant to occupational performance at Google (e.g., not being able to handle stressful assignments), the size of these negative emotion sex differences is not very large (typically, ranging between “small” to “moderate” in statistical effect size terminology; accounting for perhaps 10% of the variance1). Using someone’s biological sex to essentialize an entire group of people’s personality is like surgically operating with an axe. Not precise enough to do much good, probably will cause a lot of harm. Moreover, men are more emotional than women in certain ways, too. Sex differences in emotion depend on the type of emotion, how it is measured, where it is expressed, when it is expressed, and lots of other contextual factors. How this all fits into the Google workplace is unclear to me. But perhaps it does.
As to sex differences in mate preferences and status-seeking, these topics also have been heavily researched across cultures (for a review, see here). Again, though, most of these sex differences are moderate in size and in my view are unlikely to be all that relevant to the Google workplace (accounting for, perhaps, a few percentage points of the variability between men’s and women’s performance outcomes).
Culturally universal sex differences in personal values and certain cognitive abilities are a bit larger in size (see here), and sex differences in occupational interests are quite large2. It seems likely these culturally universal and biologically-linked sex differences play some role in the gendered hiring patterns of Google employees. For instance, in 2013, 18% of bachelor’s degrees in computing were earned by women, and about 20% of Google technological jobs are currently held by women. Whatever affirmative action procedures Google is using appear to be working pretty well (at least at the tech job level). Still, I think it’s important to keep in mind that most psychological sex differences are only small to moderate in size, and rather than grouping men and women into dichotomous groups, I think sex and sex differences are best thought of scientifically as multidimensional dials, anyway (see here).
Not exactly a ringing endorsement of Damore’s use of his research and the data on increasing “status” vs “things” jobs suggests women might have been settling for those jobs only as they were in enforced gendered roles. Schmitt also seems to agree, extraordinary claims require extraordinary evidence, and these effects are small. Linking gendered behavioral differences to massive differences in performance in tech or engineering is an enormous stretch of logic. Schmitt emphasizes uncertainty, and the need to recognize complex role of gender on human behavior, he sure sounds like a scientist (for an Evolutionary Psychologist 😉 ).
The one who doesn’t sound like a scientist is Damore, who it turns out falsely claimed to have a PhD, gave his first interviews to alt-right youtubers, compared Google to Soviet prison labor camps even wearing a “Goolag” shirt for his WSJ editorial. He sounds less like a scientist, and more like he’s read the Crank Howto. I don’t understand how he ever expected to keep his job, after it turns out he did not have a PhD, he blasted a crank manifesto at his workplace that demeans a significant portion of the Google workforce, managed to embarrass his company on a national level, and ultimately demonstrated fundamental incompetencies in analysis and workplace etiquette. He would probably benefit from some training along the empathy axis, but instead is nursing a google-sized persecution complex.
To summarize, a junior, not-PhD employee of Google has written a 10 page document which purports to explain that the massive imbalance in male:female ratio at the company is not necessarily due to historic struggles of women for equal representation in equality, readily measurable bias, or structural sexism, but is instead due to female biology. The evidentiary basis for this argument is 3 bullet points followed by 3 short paragraphs that cite a few wikipedia pages, some libertarian/rightwing opinion pieces, a handful of papers on gendered differences in behavior showing some interesting but small differences between men and women, a bizarre reference to data from males castrated at birth (please someone find me that paper), some handwaving about male/female sex selection and “status” belied by a 40 year trend in women increasingly taking higher status jobs, and a borderline sexist psychological theory about “masculine brains” with similarly small differences between men and women. Notably, all of his arguments are dependent on the assumption that the male brain is fundamentally better at engineering because they got these jobs first and are thus appropriately over represented, and qualities like empathy and interpersonal skills don’t contribute to what is already a flawlessly healthy corporate culture in tech. By this logic women don’t do well in this culture because female cognition is inadequate to the task, not because it’s hard to fit in as a woman in at the boys club.
He does not discuss or cite any of the extensive literature for the constant measurable bias women undergo in the workplace. His argument dismisses the more probable negative effects of historical oppression of women (denial of the vote, of property, of jobs, of education) well into the last century as well as ongoing structural sexism. He fails to link these effects to actual performance or interest in software engineering, he grossly oversimplifies the relationship between culture and behavior in favor of radical biological determinism, and wraps it into a typical Panglossian “just-so” story.
After predictably being fired for sending a crudely-argued, c-grade essay on why “girls like talking not math”, he has now made the rounds of the alt-right internet, the antediluvian editorial page of the WSJ, and has cried persecution at Google comparing himself to a slave laborer. He denies he’s an ideologue, even though as example of left wing denialism he cites John Tierney of the Manhattan Institute, and his argument that global warming scientists are the real threat to science (plus Rachel Carson DDT revisionism – yay!). By their fruits you shall know them.
What this shows is, the people who are impressed by his line of argumentation and series of events are ideologically-primed to accept it, not that they are particularly good judges of science. Pay attention to who buys into this uncritically, it’s better evidence for weak, sexist minds than it is for weak minds of a sex.
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