The problem is that you don’t have the luxury of just being skeptical. I mean, you do if you’re just writing a scientific paper. But you don’t if you’re making actual decisions that impact people’s lives. In that case, what counts is what’s most likely, not what you can prove.
In this case, what’s at stake is whether the playing field should be tilted in the direction of favoring of women. If this is just counter-tilting a field that’s currently tilted in favor of men, such that it should level out, then that’s one thing. But if the field is not tilted in favor of anyone, and you’re making it tilted in favor of women, then you’re discriminating based on gender. And I assume we all agree that discriminating based on gender is a Bad Thing.
So you don’t get the luxury of saying “I require a high level of proof before I assume there are differences”. That’s like saying “I require a high level of proof before I stop discriminating based on gender”. You need to go with what’s most likely, on balance.
Actually, this whole thread makes me pretty glad that China is going to become the most powerful country in the world at some point (they’ve already overtaken the United States in some fields, like ‘number of papers published per year’ in certain scientific fields). China seems to take a more healthy and value-free approach to the world, without some of the political shibboleths that plague the United States.
In any case, I’m not sure why you would think women would be less likely to believe in innate gender difference. I no longer believe the Black-White test score gap in the US is partly genetic, having been convinced otherwise, but Black Americans are actually more likely to believe that the gap is due to genetics than white people are.
That’s reasonable. If Google had very few white men, or considerably less than the population at large, then I’d consider that maybe their policy is discriminating against white men.
If you want men and women to fill up different occupations at exact parity you are going to have to change how men and women are made and develop at a biological level.
Just because people have the same potential to perform a job doesn’t mean they will seek to do it, other factors will sway their paths, and biology is one of the things responsible for that.
We’ve replicated racism and misogyny through quite a few decades of scientific advancement. We look back now and laugh at phrenologists, but people have made essentially the same mistakes both before and after. Given that history, and given our knowledge of how we imperfect humans operate, it is entirely prudent to be more skeptical of those results that confirm longstanding social prejudices. That is precisely how you get to “what’s most likely” instead of “what makes white dudes feel better about their undue social standing.”
Thank-you. I withdraw my assertion that there were no citations. The couple versions I found appear to have been copy/pastes without the hyperlinks from the original.
I still think his understanding of what empathy is and it’s role in the workplace and as a tool are grossly incorrect or misapplied. His stated desire to “understand how and why people think the way they do” is at odds with everything else in that section, from the header to the conclusion. It’s of no value if you understand at an academic level that a person is belittled by being called “sweetie” but you refuse to empathize and understand emotionally why that might cause them to leave the company/field.
I’m more referring to my hope that in a China-dominated world there will be less emphasis on judging countries by their adherence to liberal values, democracy, human rights record, etc. and more on “we do business with whoever wants to do business with us”, rather than on Chinese attitudes towards gender per se.
That doesn’t logically follow, or address the question.
You know that Google is doing things to help out women specifically. The question is whether there prior playing field biased in favor of men. You know that there’s a disproportionate number of men employed there. The question is if that disproportionate number is the result of a biased playing field, or possibly due to biological factors. If you don’t have any basis for assuming that there are no biological factors, then you have no basis for deciding that the playing field is biased in favor of men.
It’s as if you decided that the NBA should actively promote white players unless someone can come up with hard evidence that there are non-bias factors which account for the currently disproportionate racial breakdown. That’s the wrong way to look at it. Before you go about actively discriminating, you need a basis to believe that you’re just leveling the playing field, rather than the other way around.
Being skeptical of how conclusive given evidence might be is not the same thing as evidence for the opposing hypothesis.
We can “look back now and laugh at” virtually every field of science from prior generations. You want to say that’s a reason to be someone skeptical of how conclusive current scientific understanding is, that’s fine. But that’s not evidence that they’re wrong either.
It’s ironic that the guy is being lambasted largely on misrepresentations of what he wrote, specially in view of the opening paragraph:
Bolding mine.
I think many people are not reading it properly (I suspect knee-jerk induced vision blurring) to see that the goals of the author are to get more diversity in the workplace, but believes there’s a dogmatic culture that actually hinders that goal by accepting (and forcefully imposing) a single ideological view of the matter.
Which population? Is the relevant population everyone in the world, everyone in the US, everyone with an IQ above 110, everyone with the skills and desire to be a software engineer?
I would think it is everyone with the skills and desire to be a software engineer who is willing to move to where Google is located. Since computer science major graduates are 18% female and the percentage of women at Google is 30% then it would seem like the men are being discriminated against.
If you think the relevant population is people in the US, white people are 75% of Americans and 61% of Google employees so it would seem like white people are being discriminated against.
The irony is not at all surprising. People have terrible reading comprehension and are more interested in virtue signalling than having a rational discussion.
It’s not about who is better or worse, it’s about people making choices based on their affinities and aptitudes.
You want everything to be 50-50 then you need to erase any form of… what’s the word? diversity among people.
There might be cultural pressure the other way too. Maybe the effect of people like, well, yourself expressing their opinions through culture, books, movies, government programs to get more women into STEM, etc., is to push women into computer science who otherwise might not have considered it.
It’s almost like we don’t think “Not to be racist but…” doesn’t actually mean what follows won’t be racist. Subbing in “sexist” doesn’t actually change that.
I especially love the “But I’m getting so many PMs in support!” ploy. That’s a new one!
If you are deciding how to proceed in a given situation, you must assess how much weight to give to each piece of evidence. If one of the pieces of evidence under consideration is some underdeveloped and inconclusive science that confirms longstanding social prejudices, you are going to reach the correct answer more often by marginally reducing the weight given to that evidence. That’s because your thumb on the scales will, on average, correct in the right direction for mistakes produced by social prejudice.
One of the lessons we should draw from the history of scientific racism is that old bigotries tend to reemerge clothed in whatever the latest scientific fashion is. It is totally reasonable and prudent to be on the lookout for that happening and be especially cautious about seeming confirmation of these prejudices. This isn’t an approach to science as such, which should proceed according to the scientific method. It’s an approach to decision-making based on inconclusive evidence while the science is still being worked out.