James Damore is not giving up. Good for him. Attacks are coming at him from all sides, and not always displayng either the logic or the fairness with which he prsented his case to Google – and now to the world at large. One commentator helpfully notes that the problem with a lot of the counters to his now-famous memo is that they inject arguments to points that were never argued in the first place. “It’s almost as if you have to presciently qualify each statement you make to inoculate. It’s f…ing ridiculous. And then they bury you in the references to scientific articles that have nothing to do with the original arguement.”

The madnss inherent in all this is that the so-called champions of diversity are denying the very reality of diversity and the so-called enemies of diversity are the very ones protecting our rights to be diverse and live with and enjoy the characteristics of our own natural gifts.

Here is Damore’s response to an article in which issue was taken with his memo and the case he made against aspects of the dominant culture in Google. The article, entitled Here Are Some Scientific Arguments James Damore Has Yet to Respond To, was answered in Damore’s characteristicaaly polite and reasonable manner as follows. He wrote, “Please let me know if you don’t think I addressed the arguments well enough.”

His implicit model is that cognitive traits must be either biological (i.e. innate, natural, and unchangeable) or non-biological (i.e., learned by a blank slate). This nature versus nurture dichotomy is completely outdated and nobody in the field takes it seriously. Rather, modern research is based on the much more biologically reasonable view that neurological traits develop over time under the simultaneous influence of epigenetic, genetic and environmental influences. Everything about humans involves both nature and nurture.

“My document was countering the notion that everything is nurture, which is what the dominant ideology at Google states. I never deny that it’s a combination of nature and nurture, just that we shouldn’t ignore nature.”

Several major books have debunked the idea of important brain differences between the sexes. Lise Eliot, associate professor in the Department of Neuroscience at the Chicago Medical School, did an exhaustive review of the scientific literature on human brains from birth to adolescence. She concluded, in her book “Pink Brain, Blue Brain,” that there is “surprisingly little solid evidence of sex differences in children’s brains.”

Rebecca Jordan-Young, a sociomedical scientist and professor at Barnard College, also rejects the notion that there are pink and blue brains, and that the differing organization of female and male brains is the key to behavior. In her book “Brain Storm: The Flaws in the Science of Sex Differences,” she says that this narrative misunderstands the complexities of biology and the dynamic nature of brain development.

“I never talk about women and men having fundamentally different brains and I mention several times that there’s overlap in the population on many of these traits.”

American businesses also have to face the fact that the demographic differences that make diversity useful will not lead to equality of outcome in every hire or promotion. Equality or diversity: choose one. In my opinion, given that sex differences are so well-established, and the sexes have such intricately complementary quirks, it may often be sensible, in purely practical business terms, to aim for more equal sex ratios in many corporate teams, projects, and divisions.

“This quote doesn’t contradict what I wrote (it even agrees with the population level differences). I agree that diversity can be useful, I just disagree with our policies.”

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 variance). 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.

“This is talking about my comment on higher average neuroticism among women. I stated it to provide a non-sexism explanation for why women on average show more anxiety on our internal surveys and why women are underrepresented in high stress jobs. These are population level statements and are never meant to apply to an individual.”

In the end, focusing the conversation on the minutiae of the scientific claims in the manifesto is a red herring. Regardless of whether biological differences exist, there is no shortage of glaring evidence, in individual stories and in scientific studies, that women in tech experience bias and a general lack of a welcoming environment, as do underrepresented minorities. Until these problems are resolved, our focus should be on remedying that injustice. After that work is complete, we can reassess whether small effect size biological components have anything to do with lingering imbalances.

“I would have to ask for actual evidence. Also, the average difference in interest in people vs. things is large (more than a standard deviation): only ~15% of women have the same level of interest in “things” as the median/average man and the proportional disparity increases as the interest increases.”

The true underlying distributions would be useful if Google’s hiring process was to select people at random from the population, put them through a standard test of the single “quality” variable of interest, then take the ones who passed the test and discard the ones who failed. As a description of how recruitment processes don’t work, this is pretty spot on. Google (like any other company — I first started making this argument in the 1990s when McKinsey were publishing their incredibly influential, amazingly wrong and massively destructive “War For Talent” series) fills jobs by advertising for vacancies or encouraging through word of mouth and recruiters, using interview questions and tests which might have unknown biases, and recruiting people for their suitability for the roles currently vacant (which is not the same thing as “quality” because companies change all the time but keep the same employees. Each one of these stages is enough of a departure from the random sampling model to mean that the population distributions are not relevant.

“Google is a huge company that hires thousands of “software engineers” a year, I don’t know why population distributions wouldn’t be relevant, especially if we take the entire tech industry into account. Someone please tell me if they find the quoted argument intelligible though.”

There are sme more good supportive comments on the Redit post in which this was published.