This defense of the "manifesto" is flawed just like the others. It picks out a small subset of the claims made in the document, discards the context and all the other claims, and then harangues us for having a problem with "science". I could argue "water is composed of two hydrogen molecules and one oxygen molecule, so women are bad at software development", and my argument would just be a difference of degree worse than hers.
We can talk straightforwardly about what makes the document problematic: whatever the validity of the "scientific" claims it makes about gender differences, there is no support (and likely no validity) to the connections it then makes to software development work. Despite that unjustified leap, the document goes on to suggest strongly that women working at Google are less qualified than men. There is no science Debrah Soh can cite to back up that assertion, however much she might want to.
Anyone can wrap an incendiary statement up in a pile of banal sentiment and ambiguous appeals to social science. When challenged, refocus the debate on the truisms and the footnotes and pretend you didn't write the nasty stuff you hid in the middle. And, as we can see, plenty of very smart people will fall for the trick.
Gender equity has been improving in the United States for several generations. As that has occurred, female participation in STEM fields (and in the professions, like medicine and law) has expanded dramatically. Many science fields are now approaching parity. Most have more than twice as much participation as computer science. That includes the field of mathematics, which is closely related to computer science and is certainly more intellectually challenging than "computer science" as practiced in the industry.
Among all STEM fields, computer science is distinguished for losing the participation of women over the last 10 years.
Unless the women of 1950 are somehow biologically different from those of 2017, the author's theory will somehow have to address the fact that her argument would have predicted the fields or law, medicine, biochem, mathematics, astronomy, statistics, accounting, and actuary would all be bereft of women over the 20th century --- obviously, the opposite occurred, despite the sexual revolution that was immediately to come.
The author of this article discusses a correlation between increasing gender equity and decreased STEM participation that does not appear in the evidence. There's a reason she does that: if you don't stipulate that correlation, the argument against gender bias in computer science has to confront another damning fact, which is that gender disparity in the field isn't global. Unless women in Asia are somehow biologically different than those of the US, her argument needs some way to address the fact that women make up the majority of STEM majors in many of those cultures.
Reading this article and then this thread, I find that there's really only two aspects of it that HN finds persuasive: the headline's appeal to "science", and the footnote observing that the author is a female scientist. That's not enough. Everything in between those things is wildly off.
In discussions about gender parity in CS, the word "preference" is a coded appeal to the Just World Hypothesis. There is a yawning chasm between neuroscience findings about "agreeableness" and "stress tolerance" and suitability for any particular kind of white-collar symbol-manipulation work. Ms. Soh must intuitively understand that, but mentions it not once in her piece, instead pretending that observations about the kinds of toys children play with allow us to reflect participation statistics directly into real preferences about work. Shenanigans.
Point #1:
"I’m not going to spend any length of time on (1); if anyone wishes to provide details as to how nearly every statement about gender in that entire document is actively incorrect,¹ and flies directly in the face of all research done in the field for decades, they should go for it. But I am neither a biologist, a psychologist, nor a sociologist, so I’ll leave that to someone else."
In other words: "I have no relevant expertise, but I just know it's wrong."
We would never tolerate that from "the opposing side." I can point you to other articles that do the same.
This is the rallying point for a lot of people, and it's the wrong place to take a stand. It's worth pointing out.
You haven't so much responded to my comment as used it as a coat rack to hang an unrelated argument on. I didn't cite Zunger's blog post and don't really understand why people like it so much. I'd prefer not to discuss it here, and thank you in advance for not demanding I do so.
If that's what I did, I didn't mean to. I was primarily addressing your opening paragraph:
> It picks out a small subset of the claims made in the document, discards the context and all the other claims, and then harangues us for having a problem with "science".
I'm claiming that the reason she does this is because it is the number one point cited by many of the detractors, and it prevents us from getting into the more interesting and relevant questions.
If you want to suggest that Debrah Soh blundered by expanding a rebuttal to Yonatan Zunger's piece into a broader defense of the very dumb "anti-diversity memo", I am prepared to stipulate that. Like I said: while I may be on the same side as Zunger, I found his blog post shrill and unpersuasive. He had the facts on his side, but didn't know where to look for them, and waved his hands instead.
Thank you for this great response. I think you've covered why the 'manifesto' cannot be supported on a scientific basis very well. There's another issue with the manifesto that I don't think is talked about as much. And that's the human element.
All the cited science in the manifesto is irrelevant in my opinion. As an engineer you work with other people. You probably spend 40 or 50 hours a week with them. They may or may not become your friends, but they will have a huge impact on your life, and you on theirs. Many of these people will be women. They are REAL people that you interact with everyday. What he did when he decided to share this manifesto at work is show that he thinks of these women as statistics, and that on average they are not as capable as he is. He has turned the women he works with everyday into a technical problem that he can solve with his intellect. That is incredibly insulting! He's shown an incredible lack of empathy and understanding towards the women he works with. He's turned them into numbers. He's shown he does not care about them as individuals. This is absolutely unforgivable.
If he had simply released this as a research project or something on the internet then it wouldn't be a problem. But he didn't do that. He didn't make a distinction between the general population and the women he works with. He shared it at work.
I understand the arguments about how valuable interpersonal skills are to software development, but I don't like talking about them for a couple reasons:
1. The effect sizes we're talking about are tiny. A randomly selected cohort of men and women can routinely be expected to produce more women innately skilled at math, or more men innately skilled at negotiation. It's one of the more galling aspects of the debate about this stupid "manifesto", which at one point redraws a well-known chart about the overlap in ability between men and women to exaggerate the difference between the sexes: at no point do any of the advocates of the "manifesto" address effect size compared to the observed disparity in the field.
2. Very little about computer science as it is practiced in the industry is tied with any rigor to any particular skill. For the most part, software development is a standard white-collar symbol manipulation job in which productivity is defined mostly by meticulousness and generic learning and pattern matching. Attempts to break down aptitude by gender tend to imagine computer science in terms of compiler theory and algorithm design, when in reality 90% of all software development is repeated iterations of "wire this database column into this UI table".
> Attempts to break down aptitude by gender tend to imagine computer science in terms of compiler theory and algorithm design, when in reality 90% of all software development is repeated iterations of "wire this database column into this UI table".
I think computer scientists tend to apply the same skills and techniques to social issues that they do to compiler theory and algorithm design. And that's how you end up with these 'manifestos'. I don't think that many people are offended by the science. They are offended that he has turned women in tech into a technical problem that he can solve with his logical reasoning.
It appears that he thinks he's the only one who's read the studies. It's arrogant. Everyone interested in the issue has already read those studies. They also have enough interpersonal skills to know that you can't just apply studies to your coworkers.
I agree that the document blurs the line between preference/aptitude and is not totally clear which is one of the reasons overall it is a mess.
Nonetheless, many of the statements in "Personality differences", "Men's higher drive for status", and "Non-discriminatory ways to reduce the gender gap", especially the stuff about women being more prone to anxiety, liking part-time work, caring more about people than things, etc. speak strongly to aptitude for engineering, given the context of the document.
EDIT: Also, I reject the implication that the claim "women have less of a preference for engineering" is not in and of itself a harmful stereotype.
1. The memo uses the word "preference" without ever establishing whether it's talking about free choice or choice after discouragement, and so is flagrantly begging the question.
2. It's simply false that the memo makes no connection between supposed preference and aptitude, as it builds to a section about the "harms of diversity" that includes a direct claim that women in Google's workforce are less capable than men.
> It's simply false that the memo makes no connection between supposed preference and aptitude, as it builds to a section about the "harms of diversity" that includes a direct claim that women in Google's workforce are less capable than men.
I have been confused why you and others have been repeating this, but after re-reading the section "The Harm of Google’s biases", I think I see your point now.
Damore does not say that all women who work at Google are unqualified, but he does imply that there are fewer women who are qualified, and that by trying to mine that population too heavily, Google is hiring women who are, on average, less qualified than the men are, on average. Do I have that right?
Damore is smart enough not to come out and say directly that he believes all women to be less qualified; instead, he just strings together a series of assertions that leaves reasonable people with only one conclusion, which is that the women at Google are beneficiaries of a lowered bar that results in the women at Google being on average less qualified than the men.
I have very little patience with arguments rooted in "but that's not exactly what he said", because I have been on message boards for approximately the entire literate span of my 40 years on this planet, and the technique of couching inflammatory assertions in half-hearted hedges and deliberately ambiguous abstractions is the oldest trick in the book.
He allocated a whole subhed to his point, and the whole document builds to it. The subhed is: "the harm of Google's biases". The biases he's referring to are towards women and against men. The harm he refers to is "a lowered bar". His point is plain.
(I'm confident people aren't going to like this comment, but it is what I honestly believe, after what I believe to be pretty significant consideration, and no part of this thread is made better by me pretending otherwise.)
Oh, so you know what he is actually thinking even though he doesn't say it and says things that are contrary to it. I see.
This, too, helps me understand the outrage. Thank you for being honest.
(I do hope the 'thank you' above can be read by people in a calm, snark-less voice. It is genuine. I appreciate Ptacek being forthright. I learned from it. It really does make the thread better and furthers the conversation in a productive way.)
> For what it's worth: your thanks might be intellectually honest, but your summary of my argument is not.
I am very open to being corrected.
When you said, "Damore is smart enough not to come out and say directly that he believes all women to be less qualified", I took it to mean that you think Damore actually believes that all women are unqualified and arguing about populations is a just a smokescreen for what he is really thinking. This is helpful to understand because it means that you don't think the memo is actually about population-level differences and so exploring that argument is a waste of time.
But if you didn't actually mean that, I misunderstood and apologize.
> The memo uses the word "preference" without ever establishing whether it's talking about free choice or choice after discouragement, and so is flagrantly begging the question.
Of course it's talking about free choice. Introducing "discouragement" in the equation is itself begging the question : it's an extra hypothesis which is unnecessary in presence of a simpler, more fundamental explanation (like this one https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166361/ ). Occam's Razor 101.
> includes a direct claim that women in Google's workforce are less capable than men.
One of the bizarre things about this controversy is that even if you grant all of the memo's assumptions (something that I won't do except hypothetically), it still wouldn't follow that _Google's_ policy was bad for Google. Google is a big company but it only employs a small fraction of software developers. They could very well decide that it's good for them to have more women represented regardless of whatever the "global" distribution is.
This is what I don't understand in all these HN threads. How can some kind of vaguely related science paper(s), albeit perhaps accurate per se, make people so absolutely sure that there's some kind of _causal & scientific_ cause to the issue at hand - as it requires a really long jump. I assume it's just confirmation bias wreaking havoc. Thanks for a good summary too.
>the argument against gender bias in computer science has to confront another damning fact, which is that gender disparity in the field isn't global. Unless women in Asia are somehow biologically different than those of the US, her argument needs some way to address the fact that women make up the majority of STEM majors in many of those cultures.
There is research finding that the more advanced a country is in terms of gender equality (by generally accepted metrics), the more pronounced the occupational gender gap actually is in most fields.
One proposed explanation is that women in advanced economies are freer of constraints and higher up in the Maslow pyramid of needs, and can afford to go into jobs they actually like, rather that whatever they feel is their duty/more lucrative/otherwise rewarding. Kind of like yuppies (of either gender) dream of exiting the corporate world to set up an organic food shop. That would certainly explain the very different STEM gender gaps in the US/Sweden vs India/China, for example.
I don't have the reference handy, but if someone can provide it, please do !
It's 1950, in a parallel universe differing from ours in exactly one way: scientists, using methods inferior to ours today but aided by good fortune, have generated essentially the same results about innate psychological preferences Debrah Soh cites in this piece, results we will stipulate as accurate.
We can predict the next 50 years with perfect clarity, having lived through them ourselves.
According to Soh's logic, as gender equality increases dramatically throughout the next 30 years, we should see reinforcement of "preferences" to avoid science fields. And yet the opposite thing occurs.
Why has Soh's hypothesis failed to predict? Why is it more trustworthy today?
This explanation is put forward for example in the Norwegian science documentary series Hjernevask, see episode 1 (The Gender Equality Paradox), available on YouTube. I don't have the time to hunt down the references right now but you should be able to find them through that one.
This piece is fatuous. Alexander perpetuates the "women like people, men like things" trope that is common to every message board discussion of CS gender disparity. He then explains away the fact that women excel in science fields by saying "what do women do with math degrees? They become teachers." He ignores that the effect remains in graduate-level math studies (women do not get math PhD's to become high school teachers) and in other STEM fields (women do not get biochem masters to become high school teachers).
He's contrived a just-so story that appeals to his audience. Which is par for his course.
1. I quoted from the middle of the article. I've read it multiples times.
2. There is a reason that particular argument is banned by the guidelines ("Please don't insinuate that someone hasn't read an article"): it's inherently uncivil.
>Despite that unjustified leap, the document goes on to suggest strongly that women working at Google are less qualified than men
Can you elaborate on this? Just because FEWER women may be qualified to work at Google doesn't mean that the ones that are are any less qualified than the men. The fact that fewer women are tall doesn't mean that tall women aren't tall for example.
>I could argue "water is composed of two hydrogen molecules and one oxygen molecule, so women are bad at software development", and my argument would just be a difference of degree worse than hers.
I fail to see how the science discussed in the memo is as irrelevant as you make it out to be. Is it really that far fetched that psychological makeup (as expressed in big-5 characteristics) and interests play a role in what people choose to pursue and what they like to do? Because software engineering is different than other occupations (such as law and medicine), it makes sense to think about what might attract one to one profession over another. Many intelligent women I know chose careers such as medicine over cs. And why not? It pays better and doesn't involve staring at a computer all day (something that not everybody enjoys). The same could be said for law and finance (investment banking, private equity).
>Among all STEM fields, computer science is distinguished for losing the participation of women over the last 10 years.
If you scroll down to the two bar charts in the link above, you'll notice that while the % of bachelor degrees earned by women in CS has gone down, the % of PHD degrees earned has actually gone up (looks to be about 40% higher compared to 1991)! I think you would agree that earning a phd in CS is much more difficult than a BS, and I think this actually shows that women are being given more opportunity to excel academically in the subject.
As for bachelor degrees in CS, it seems like it has converged more to the % awarded in engineering. Speaking more on the differences between CS (i.e Bachelors level CS that leads to SWE jobs) and Math, I would say there is a qualitative difference between the two, and certainly one can have personal preferences. Software engineering is much more about creating systems that work and solve real-world problems. It also involves a lot of programming. Pure math (and theoretical CS) is more about investigating an abstract world and looking into interesting patterns and connections. It actually has a lot of similarities with philosophy in this regard. Some of the female math/science majors I knew actually didn't really like programming and ended up being highly successful in other fields even if they went into industry (medicine/finance/business).
>There's a reason she does that: if you don't stipulate that correlation, the argument against gender bias in computer science has to confront another damning fact, which is that gender disparity in the field isn't global. Unless women in Asia are somehow biologically different than those of the US, her argument needs some way to address the fact that women make up the majority of STEM majors in many of those cultures.
"Regression analyses explored the power of sex, gender equality, and their interaction to predict men's and women's 106 national trait means for each of the four traits. Only sex predicted means for all four traits, and sex predicted trait means much more strongly than did gender equality or the interaction between sex and gender equality. These results suggest that biological factors may contribute to sex differences in personality and that culture plays a negligible to small role in moderating sex differences in personality."
From my personal experience (which I agree is less convincing than the numerous empirical studies that have been done on the topic), I'll say that many women in asian countries are pushed into studying cs/programming even if they don't like it, because those fields often provide a straightforward path to making a decent income.
We can talk straightforwardly about what makes the document problematic: whatever the validity of the "scientific" claims it makes about gender differences, there is no support (and likely no validity) to the connections it then makes to software development work. Despite that unjustified leap, the document goes on to suggest strongly that women working at Google are less qualified than men. There is no science Debrah Soh can cite to back up that assertion, however much she might want to.
Anyone can wrap an incendiary statement up in a pile of banal sentiment and ambiguous appeals to social science. When challenged, refocus the debate on the truisms and the footnotes and pretend you didn't write the nasty stuff you hid in the middle. And, as we can see, plenty of very smart people will fall for the trick.
Gender equity has been improving in the United States for several generations. As that has occurred, female participation in STEM fields (and in the professions, like medicine and law) has expanded dramatically. Many science fields are now approaching parity. Most have more than twice as much participation as computer science. That includes the field of mathematics, which is closely related to computer science and is certainly more intellectually challenging than "computer science" as practiced in the industry.
Among all STEM fields, computer science is distinguished for losing the participation of women over the last 10 years.
Unless the women of 1950 are somehow biologically different from those of 2017, the author's theory will somehow have to address the fact that her argument would have predicted the fields or law, medicine, biochem, mathematics, astronomy, statistics, accounting, and actuary would all be bereft of women over the 20th century --- obviously, the opposite occurred, despite the sexual revolution that was immediately to come.
The author of this article discusses a correlation between increasing gender equity and decreased STEM participation that does not appear in the evidence. There's a reason she does that: if you don't stipulate that correlation, the argument against gender bias in computer science has to confront another damning fact, which is that gender disparity in the field isn't global. Unless women in Asia are somehow biologically different than those of the US, her argument needs some way to address the fact that women make up the majority of STEM majors in many of those cultures.
Reading this article and then this thread, I find that there's really only two aspects of it that HN finds persuasive: the headline's appeal to "science", and the footnote observing that the author is a female scientist. That's not enough. Everything in between those things is wildly off.
In discussions about gender parity in CS, the word "preference" is a coded appeal to the Just World Hypothesis. There is a yawning chasm between neuroscience findings about "agreeableness" and "stress tolerance" and suitability for any particular kind of white-collar symbol-manipulation work. Ms. Soh must intuitively understand that, but mentions it not once in her piece, instead pretending that observations about the kinds of toys children play with allow us to reflect participation statistics directly into real preferences about work. Shenanigans.