Random Thoughts on the Google Memo
I haven’t been following the Google Memo saga all that closely, but I do have some random thoughts about the whole brouhaha:
1. If the distribution of skillsets, interests and temperament is the same between men and women, why do the latest figures (June 24, 2017) from the Bureau of Prisons indicate that 93.3% of federal prisoners are men?
2. Would a rational person, upon learning that 93.3% of federal prisoners are men, jump to the conclusion that our legal system won’t punish women for crimes?
3. If the distribution of skillsets, interests, and temperament is the same between men and women, why does Google give advertisers an option to target customers by gender? Shouldn’t they stop?
I note… this post was suggested by my wife. She asked my opinion about the topic, I made the three points above, and she said: “This sounds like it should be a post.” So here it is.
A quick follow-up. I note that Google released its workforce demographics as of Jan 2017 (https://www.google.com/diversity/)
Men make up 69% of its global workforce and presumably about 50% of the world population, so they are overrepresented by (69%/50%) – 1 or about 38%.
By contrast, they provide racial demographics for the US only. They list Asian demographic as making up 35% of their US workforce. Asians are about 5.6% of the US population, making for an overrepresentation of 525% which greatly exceeds 38%. (If you are wondering, the White demographic makes up 56% of their US workforce, which makes them underrepresented at Google.)
I’m going to guess that Google’s position on the memo linked to in the post is directly contrary to any position they would have to hold if/when someone sues them for discriminating against non-Asian people.
I recently re-read the Foundation trilogy. Hari Seldon would tell us that leaving out random chance at the individual level, there’s a certain inevitability as to how these trends will play out.
It was very politically incorrect of you to notice this.
And you have compounded your error by informing others.
That was not kind.
Oh, the shame of it all.
1. Federal prisons house less than 200,000 inmates, while state and local prisons house about 2 million. They don’t count?
2. Is crime genetically based? Maybe women are smarter to avoid being caught, which makes men stupider? Or just testosterone driven? You know, more hormonal. (wink)
3. Is the Google kerfuffle about quotas as you imply? I saw no claim to such even from Jim’s PC ghosts.
Interesting article on a Google firing of an engineer. http://www.msn.com/en-us/news/us/google%e2%80%99s-fired-%e2%80%9cpolitically-incorrect%e2%80%9d-engineer-has-sparked-a-broad-ideological-debate/ar-AApMHCK?li=BBnbcA1&ocid=spartanntp There is evidence on men in prison.
A large majority of the skirt- and dress-wearing population in America are female. Using the logic of this post, this fact could equally be used as support for assuming an innate inability of most women to excel at coding, right?
“The U.S. incarcerates 693 people for every 100,000 residents, more than any other country. In fact, our rate of incarceration is more than five times higher than most of the countries in the world. Although our level of crime is comparable to that of other stable, internally secure, industrialized nations, the U.S. has an incarceration rate that far exceeds every other country”
Maybe we are just very selective in a not natural way? this stuff sure makes it hard for us to look to normal.
Bev would tell you the same as what I am going to say and had hoped I did not have to say and avoid discussion. The justice system for the US thrives on not going to trial. 85%, 90% of all cases coming to court involving criminal activity are plea bargained. “Plea bargains are extraordinarily common in the American legal system, accounting for roughly 90% of all criminal cases.” However I am low, Here is what Judge Ratkoff has to say on the topic; “Ninety-seven percent of federal convictions and ninety-four percent of state convictions are the results of guilty pleas.” The innocent plea – bargaining estimate is 2% to 8% of the 2.2 million in prison as of 2014 or 44,000 to >160,000.
Scalia once said that those sent to their deaths falsely was extremely small and less than 3 tens of 1% or the system was 99.7% right. The flaw in his calculation as done by some idiot Oregon prosecutor was using all felonies to calculate his result. In the end, the percentage rose to ~5% being falsely murdered by the US justice system. So what is wrong here as my associate on AB appears to miss?
Plea bargaining is not a trial by a court. A Plea Bargain is an admission of guilt. Why would so many amongst all of those people of which a large portion of are minorities ever plea bargain?
– The cost of going to trial exceeds the financial capabilities of a defendant. This would be prevalent amongst minorities who have a larger ratio incarcerated.
– Public Defenders are over burdened with cases and the state pays them less than what they could make in private practice.
– Prosecutors encourage pleas as their case loads are high, this is one way to clear them out, and they work half as hard.
– It is no secret if you go to trial and practice your constitutional right to a “fair” trial, prosecutors and the courts have to work and more than likely the sentence will be harsher than if you plea bargain.
None of this is a secret. It is well know about the court system, the costs, the time spent, and the end result. Is it worth it? It was for me to spend $100,000 which in the end went to SCOTUS with the aid of a University of California – Irvine Dean of the School of Law. How many people have the resource to do this?
So lets head this off as it is a silly topic. ~50% of the imprisoned or jailed are there due to nonviolent crimes, drugs could be the prevalent reason. Of those imprisoned or jailed 1 of 10 is black on any given day. 59% of the state prison population are minority males. Yes, males do commit more violent crimes. Younger males until that brain matures are more likely to make stupid decisions. Black Americans are imprisoned at a rate of “5.1 times the imprisonment of whites.”
But why, why are Black Americans more likely to go to prison? “Other factors, not simply race, account for differences in crime across place. Criminologists Ruth Peterson and Lauren Krivo note that African Americans comprise a disproportionate share of those living in poverty-stricken neighborhoods and communities where a range of socio-economic vulnerabilities contribute to higher rates of crime, particularly violent crime.44) In fact, 62% of African Americans reside in highly segregated, inner city neighborhoods that experience a high degree of violent crime, while the majority of whites live in “highly advantaged” neighborhoods that experience little violent crime>.”
Crime is down in the US contrary to what one orange-haired SOB says; however, it still exists and minorities are the greater target of it and the resulting applied mis-justice. I recommended a book on Angry Bear once as written by a physiatrist Dr. James Gilligan. “Reflections on a National Epidemic, Violence.”
Joel, just don’t wear a dress when coding…. 🙂
I’d say more like locked up criminals. I note that doesn’t happen just here. I imagine there is no country in the world where male prisoners don’t routinely far outnumber female prisoners.
But I bounced around twitter too and stumbled on this. I cannot speak for anything else she has done or said, having no familiarity with her other work, but the graph she tweeted is a fine representation of the view opposite of what you seem to espouse.
Do you believe that men are only over represented in Federal prisons? Or only in American prisons as opposed to abroad as well? Or only at this point in time? Is the ratio different in times and places that incarcerate a smaller percentage of people?
I note that males have been shown to engage in more violence than females among other species of primate, and at about the same ratio that we see in humans. Thus, if our chimpanzee cousins had prisons, there would probably be male chimps outnumbering female chimps in their jails too.
I am no biologist but I think it’s safe to say none of this – in people or chimps, now or in the past- is being caused by what broadcasters show on American television.
Also… the fact that there is an overwhelming dominance among male prisoners says nothing about whether any individual man or woman is going to be a prisoner. Every individual is different. Group differences only matter to the extent they tell us we shouldn’t get exercised by the fact that men are likely to far outnumber women in prison.
Just curious … you posted “if then why…” type questions but provided no answers of your own or your wife’s.
Also the Google issue relates to predominantly 4 year degreed professionals, or in majority of cases, Masters and Doctorate degreed persons, male and female. (I know a few Google employees personally as well as some in the higher executive ranks so I know the educational pedigree of most Google employees — not of subcontracted professional services they hire, however)..
How does the general population statistic of those convicted of crimes relate to Google’s gender differences?
Please explain your reasoning and rationale for making criminal statistics a basis for Google’s gender issues, whatever they are..
Also it should be noted that in Silicon Valley high tech companies, including Apple, Linked-In, Netflix Development, Amazon Research, Oracle, etc, etc. etc. every one of these company’s are competitively hiring as many people , from both male and females candidates from the same pool available both locally and nationally.
Thus whatever is going on at Google (or not going on) regards male v female capabilities is common to all high tech companies in the Valley, and SF Bay Area in general, and by extension to the rest of the nation in professional employment as well.
To get an idea of the available pool of candidates, try using the national engineering graduates ratio of males to females.
I’m at a complete loss of how you rationalized and reasoned that national crime statistics (incarcerations) of male v female relate to professional 4-year, masters, and doctorate level employees at Google (or any other set of high-tech companies).
Is there some relationship you’re attempting to infer? Please explain the relationship.
Kimel said: “Would a rational person, upon learning that 93.3% of federal prisoners are men, jump to the conclusion that our legal system won’t punish women for crimes?”
I’m not sure this was your intent, but if Google were to use your statistics, they rationally should refuse to hire any men because they are so prone to criminality.
This is precisely the way that that jerk at Google was using statistics to “prove” that women have less aptitude for software jobs.
People like Damore are toxic poison to good working relationships in a company. They do more damage than they are worth so Google had good economic reasons to fire him. Goodbye and good riddance to bad rubbish.
Most, but not all, of the women I have known in my life would never ever train for, or want to be in, an engineering field.
Most, but not all, of the men I have know in my life would never, ever train or want to be in, a caregiving field.
It’s probably a combination of aptitude and preference. It’s not politically correct, but it is true. So which is more important, PC or truth?
Don’t forget the monkeys.
I linked to the memo so there’s no reason to make stuff up about what it says or take some third party’s word.
Follow the link. If 10 pages is too much to read then I think this is a good summary of what the guy wrote:
I would also suggest a gander at this tweet to which I linked up thread:
Finally, to paraphrase myself:
Kimel: “I hope it’s clear that I’m not saying that diversity is bad …”
You know that is exactly the same phrasing as every bigoted racist rant I’ve ever read: “I am no racist but …” Just because you say the magic words at the beginning it doesn’t negate the actual bigotry of the words that follow.
Kimel: “I would also suggest a gander at this tweet to which I linked up thread.”
Thank you. You have clearly demonstrated your bigotry very nicely with that figure. That ridiculous bimodal distribution in your example above is exactly the way a bigot views the world. I would suggest you read one of the papers that the jerk linked to:
The conclusion is that gender differences on the Big 5 personality traits are very small and some are nearly undetectable.
Here is the actual bimodal distribution they found for “agreeableness”, which had the biggest difference:
As you can see there is much more overlap than there is difference. But the fact that you view the differences as in your ridiculous tweet example is a classic demonstration of your bigotry — imagining tiny differences as being huge. That’s the world view of a bigot.
I asked you a direct question as you’ll note in my comment at 6:41 pm.
It was, for your reference:
“I’m at a complete loss of how you rationalized and reasoned that national crime statistics (incarcerations) of male v female relate to professional 4-year, masters, and doctorate level employees at Google (or any other set of high-tech companies).
Is there some relationship you’re attempting to infer? Please explain the relationship.”
Will you or will you not answer a direct question with a direct answer?
Go find a biologist and ask her or him whether two populations that have different physical characteristics, different reproductive strategies, different hormones, different brain anatomy, and different mutation rates (remember high school biology and the XX v. XY chromosomes???) should be expected to have exactly the same distribution when it comes to, say, interest in programming or criminal tendencies. And then go ask BillB whether the biologist is bigoted.
I suggest you re-read. Here’s the first paragraph from their conclusion:
That isn’t remotely compatible with your earlier comment.
As to the graph… five point scaling compresses things a lot, but it is evident the two distributions are different. The average woman is more agreeable than the average male by their way of measuring. I’m not sure what your difficulty is with understanding the graph.
Here is an article from a source I imagine you will find to be ideologically acceptable. Here’s how it starts:
Is it bigotry for the Huffington Post to report that male monkeys and female monkeys prefer different toys? What about concluding that human kids behave the same way? Is it really bigotry to notice something that amounts to basic biology? Do you think the monkeys are being affected by the evil American patriarchy that seeks to impose the Handmaid’s Tale lifestyle on women in the US and oppress the freedom loving men around the world into imposing that same lifestyle on the women in other countries?
To be more succinct:
There is Kimel’s view of the world:
Here is reality:
That, in a nutshell, is the definition of a bigot — a distorted view of reality.
I am hugely curious – I’ve also noted upthread that in any society I can think of, at any time period I can think of, incarcerated men far outnumber incarcerated women. It seems to happen consistently. Why do you think reality works like that? And how do you think it would look on a graph?
Because your reality is that you haven’t been able to properly interpret a graph in a paper nor did you get the point of the graph of the tweet. But even you presumably understand that the differences in incarceration rates are huge and reasonably consistent temporally and geographically.
Also, about the monkeys playing with different toys depending on their gender – is that bigoted? And if so, is it the monkeys who are bigoted, or the people who made the observation, or is it just me for repeating it?
“Go find a biologist and ask her or him whether two populations that have different physical characteristics, different reproductive strategies, different hormones, different brain anatomy, and different mutation rates (remember high school biology and the XX v. XY chromosomes???) should be expected to have exactly the same distribution when it comes to, say, interest in programming or criminal tendencies. And then go ask BillB whether the biologist is bigoted.”
I’m a biologist. Specifically, a PhD geneticist.
There is no doubt that normal human males and females differ in hormones, reproductive strategy and in sex chromosome karyotype. I’m not up to speed on CNS anatomy, but for the purposes of this discussion, we can stipulate measurable differences.
From the POV of a scientist, the null hypothesis is that there are no necessary gender-specific differences in cognition. That doesn’t mean there aren’t, it means that burden of proof lies with those who assert a domain of behavior that is biologically determined to falsify the null hypothesis.
The assertion that there are biologically based gender differences in computer coding ability does not, to my knowledge, rise to the level of evidence capable of falsifying the null hypothesis. But as a scientist, I’m happy to learn of the controlled scientific studies that buttress the assertion. Take all the time you need.
Glad you showed up to comment. Beats dealing with people hurling epithets.
Unfortunately, today is going to be a long day at the office, so time is short, but this is fairly easy. I used to do some design statistical algorithms for cost estimation software. My career path is different now, but I still trot R now and again. Part of that is being able to visualize how data is going to look in time and space before I start a project. I’m not always right, mind you, but if I were wrong too often I wouldn’t earn a living.
Now, there are plenty of studies about differences in teenagers and adults (girls are said to be better at verbal skills, on average, for example), but someone can always chalk those up to social pressures, etc. But that cannot be true of brain structure, or cognitive differences at very, very early ages:
You also see similar issues at the other end of the age spectrum. One wouldn’t expect alzheimer’s patients, for instance, to be driven by social norms, but gender differences exist there too.
That said, I am curious. You mention that you grant differences in nervous system anatomy, hormones, reproductive strategy, and chromosomes. But you feel that the burden of proof lies in those who feel those factors make a difference. I am not nor have I ever claimed to be a scientist, but that assertion seems odd to me given what you grant.
I am not meaning to be insulting, but to me it is equivalent to granting the same information you did and then stating the burden of proof lies on those who believe “men and women are likely to have different ability to run a marathon” or “men and women are likely to have different ability to swim 100 meters” or “men and women are likely to have different ability to lift weight.” What am I missing here? (I note that some of the same people who will tell you there are no average differences in the distribution of cognition between gender populations will say the same about physical capabilities.)
Quick follow-up. I guess what I am stating is that if I was designing an experiment, and I would want test and control samples to be as similar as possible along all dimensions exception the one tested. But if your null is that two groups have the same cognitive function, and you have reason to believe that the two populations have (on average) different brain anatomy and different hormones, heck, it almost looks like a deliberate attempt to bias the data to falsify the null.
“I am not meaning to be insulting, but to me it is equivalent to granting the same information you did and then stating the burden of proof lies on those who believe “men and women are likely to have different ability to run a marathon” or “men and women are likely to have different ability to swim 100 meters” or “men and women are likely to have different ability to lift weight.” What am I missing here? (I note that some of the same people who will tell you there are no average differences in the distribution of cognition between gender populations will say the same about physical capabilities).”
You aren’t missing anything. Assertions that men and women differ in any respect, including karyotype, must bear the burden of proof. As it happens, karyotypes and now whole genome sequencing provide overwhelming evidence to falsify the hypothesis that men and women have the same karyotype. I’ll bet there are similar data to falsify the hypothesis that there is a biological basis for differential performance of men and women in marathons and weight lifting.
But to assert that because we can agree that there exists dispositive evidence for gender differences in one or more domains (karyotypes, extreme sports) exempts you from the burden of proof that men and women differ in computer coding ability is a non sequitur. The burden of proof still rests with the person making the assertion that such a difference exists. On this thread, you have not met that burden, no matter how many irrelevant examples you cite.
karyotypes? I am not familiar with this term.
“I would want test and control samples to be as similar as possible along all dimensions exception the one tested.”
Yep. Matched controls. A good scientific study includes ’em.
“But if your null is that two groups have the same cognitive function, and you have reason to believe that the two populations have (on average) different brain anatomy and different hormones, heck, it almost looks like a deliberate attempt to bias the data to falsify the null.”
I thought your hypothesis was that men and women differ biologically in their abilities in computer coding. If not, my bad. Please state your hypothesis.
But if I got your hypothesis correct, then the two populations that must be compared are men and women. That’s not bias, that’s the experiment. Match the men and women for age, education, and other parameters that might be relevant. But you cannot match men and women by gender.
If your hypothesis is (1) that sex differences in spatial-visualization ability in 2- and 3-month-old infants results in differential computer coding ability in adulthood, and/or (1) that sex differences in risk for Alzheimers is linked mechanistically to gender differences in computer coding ability, those are, in principle, testable (and potentially falsifiable) hypothesis. Have peer-reviewed studies been done to address those connections? If not, merely asserting the measurable difference among infants or Alzheimer’s patients mean that there necessarily exists a difference in computer coding ability is a non sequitur and is not scientific.
Is it biologically and environmentally possible for any two humans who are not identical twins to have and/or be tested to have the identical “skillsets, interests and temperament” at the same point in time?
I submit therefore that because there is no such thing as human clones who have experienced identical environments and experiences to a given point in time there can be no two humans who have identical “skillsets, interests, and temperament”.
Thus, by extension, there exists a very wide diversity of skillsets, interests, and temperaments among humans
Employers, potential mates, potential friends and associates, therefore seek to find those specific skillsets, interests, and temperaments in other humans which suit or closely match their objectives in those pursuits.
And unless the objective is identical for each such person pursuing another human to meet the objective then by definition they are seeking different and diverse skillsets, interests, and temperaments.
And because each human is not a clone of another in every respect, then it is a-priori the objective of the pursuit to find those humans who most closely match the objective skillsets, interests, and temperaments from the available pool of humans at that time.
Therefore the closest matches of available humans to the objectives will by definition and necessity have a diverse set of skillsets, interests, and temperaments.
Then it only becomes a question of what the pursuit’s objectives are in selecting employees, mates, friends, and associates with the closest match to those objectives from the available pool of humans.
What does this have to do therefore with gender?
Can if be asserted that one gender is more closely matched to the pursuit’s objectives than the other or that the available pool of closest matches can be distinguished primarily by gender?
The answer to this is that only the pursuers objectives can answer that.
Thus in the Google case, as well as any other employer’s case, Googles objectives are clearly not the same as one of it’s employee’s objectives. The employee’s (and perhaps many other employees) objectives are not those of Google’s. It seems incumbent on Google therefore to either change the employees objective to match Googles’, or to dismiss / replace the employee (and employees) whose objectives do not match Googles closely enough.
I cannot therefore find fault with Googles’ dismissal of the employee who intentionally stated for wide public consumption their own mismatch with Google’s objectives if that employee was unwilling to change their own behavior (skillset, interests, or temperament) to match Google’s objectives.
There is also another issue here and this may be the source of the controversy in fact.
The other issue is that there are federal and State laws against discriminating employment by gender. In as much as the employee was openly and intentionally discriminating their fellow employees capabilities of employment suitability by gender then they were advocating both that Google change its objectives to discriminate by gender, as well as their own allowance to discriminate among other fellow employees by gender.
Google is legally not allowed to discriminate by gender however.
On the other hand the employee who discriminates by gender is not legally bound by the law to not discriminate by gender. So legally they are entitled to so discriminate, but only if they are not in a management or any other position which can or does have influence over other employees advancement, promotions, salaries, or jobs.
But Googles’ objectives are clearly spelled out to all employees, even to the point of having special and frequent meetings on acceptance of diversity (including gender diversity) in the workplace.
Therefore the employee’s discrimination by gender is clearly not matched to Google’s objectives, which thus gives Google the option to either decide the mismatch can be resolved by the employee changing their skillset, temperament, etc) or by dismissing the employee because of the mismatch to Google’s objectives, which not coincidentally includes Google’s legal requirement to not discriminate by gender.
Thus it seems to me that one of the issues being inferred by the comments on this thread and topic is a political libertarian view of individual freedom to express an allowance of discrimination by gender in the workplace, which expression is also evidence of actual and /or potential allowance of discrimination by gender in the workplace… which is illegal.
Thus the inferences of some of the comments and as best In can fathom the author’s (Mr. Kimel) view is a libertarian belief that the law against discrimination by gender is not legal or should not be legal for individuals to discriminate by gender in the workplaces, and hence the Civil Rights act is illegal on libertarian belief system grounds.
A karyotype is the number and visual appearance of the chromosomes in the cell nuclei of an organism or species.
I wonder if the Google guy has Asperger’s syndrome? I know it’s a common, and sometimes sought after, trait in code writers. I also believe it’s much more likely to be diagnosed in males than females (there’s a huge debate as to why). It might explain his indelicate memo.
silly of me to join this, but
my opinion (i do not claim that it is dispositive) based on the limited evidence i have seen, is that google overreacted to the memo, maybe for reasons not so far published, maybe because of an hysterical insistence upon “politically correct” speech.
my opinion (still not the word of god) is that the memo was more about the PC restrictions on workers speech than it was in any way a claim that [all] women were inferior at coding. as such, the google reaction seems to have proved his point.
i once said something stupid about women mathematicians. i have learned since then that i was wrong.
my daughter has a PhD in molecular genetics. she agrees with me that women are different from men. she also happens to have developed the art of caring to a near science.
i used to be a fair mathematician, but was stupid enough to try to apply it to a “soft” field, until i gave it up because i didn’t care enough.
why all this? because i didn’t see in any of the above anything that approached ‘science’ even, much less “reason.”
i think that taken all in all people are much better off with their superstitions and prejudices than they are with their pathetic appeals to “science.”
Science works fine where it is science. Not so well when it’s reputation is invoked, or its methods applied half assedly, in disputes that have nothing to do with science.
Actually, his argument was more of the form: 90+% of criminals are men, so we should exempt women from prosecution and not have any prisons for women. Female criminals are so rare and anomalous that they and their criminal behavior should be ignored by our legal system. It’s easy to back this up with charts showing the bi-modal distribution and so on, just as it is easy to argue that Google shouldn’t be hiring female programmers.
It helps to remember that most programmers were women into the late 1960s. In fact, computer programming was invented by a woman. The whole men=math thing that is used to discourage women from studying programming is cultural. In Eastern Europe, for example, women are expected to do well in math, so they do. It’s an old custom. I remember one young Russian woman of good family telling how her nursery was wallpapered with the pages of a calculus textbook, so that when she started learning mathematics in earnest, shortly after menarche, all the formulas seemed familiar to her. Of course, all young women of good family studied calculus.
P.S. Good grief. What a sexist spell checker I have. “Menarche”, it seems, is not a word.
” she agrees with me that women are different from men. ”
Nobody on this thread denies that women are different from men. Did you have a, you know, point?
yes I did. but i didn’t expect you to get it.
real scientists are intuitive. just like women.
Now you’re just trolling.
Grow up, Dale.
Not just coding. STEM. I only quickly skimmed this paper but it seems to be a reasonable summary of the state of the research:
I agree with this.
I don’t even want to guess how anyone would conclude that someone who has written as much about externalities as I have can be a libertarian. Thinking about your nonsense gives me a headache.
Your asperger comment is a better, more succinct response to Joel than I managed.
Reread the memo. It even offers suggestions for increasing the representation of women at Google. You could argue whether or not its suggestions make sense or are implementable (I don’t think they are) but the author clearly believed he was making useful suggestions to increase the number of women at Google.
The left has a disturbing tendency to kill its own these days.
I looked at the abstract. This paper says nothing about spatial aptitudes in infants. Note that the authors recommend spatial aptitude tests, not gender assessment.
Meanwhile, here’s a thoughtful take on the Google mess:
I linked to the the Stanford page mentioning that gender based differences in spatial visualization ability were observable among 2 and 3 month old infants, well before socialization would kick in. I also mentioned my own experience that those skills are vital for building statistical algorithms.
On August 10, 2017 9:40 am you said fine and good but asked if there any peer reviewed evidence that such skills affect coding ability.
I provided a link to a paper discussing the importance of spatial visualization skills for STEM as a whole. (I note this is well known to be true. I have seen studies of the importance of spatial visualization for fields ranging from physics to surgery.)
In other words… there is evidence from Stanford that spatial visualization skills differ by gender and a separate paper stating such skills are needed for STEM. If those two are correct, then you have evidence of gender based differences at STEM.
A single paper looking at gender based spatial visualization differences in 2 and 3 month olds and how well those same individuals perform at STEM a few decades later, which is what you seem to be requesting now, is setting a standard I doubt can be met at this time. Sure, it would be nice to have, but it is also moving the goal post quite a long way.
As to the Vix piece, I only skimmed it but to be frank, it makes me worried that someone can teach computer science at Stanford and have such a poor grasp of what statistics mean. Worse, it implies a failure to visualize data. There is a lot wrong with the piece but the biggest issue to me is the whole “Google isn’t average” bit. In general, in the real world, if two real world populations are described by similarly shaped distributions which are not truncated, the difference between the two means is going to be smaller than the difference between two 99th percentile observations. I can envision exceptions and if I give it enough thought I could probably come up with one of those exceptions that actually does exist, but it isn’t something you see wandering around in the wild every day.
Think of it this way… say I have a group of forty year olds and a group of twenty year olds. The average 20 year old can outrun the average 50 year old in the 100M. With enough training and favorable enough conditions, that average 50 year old might manage to outrun the average 20 year old once or twice. But the best 50 year old will never outrun the best 20 year old. It cannot be done.
The fact that Google doesn’t hire average individuals means that any small variance that exists between groups will be magnified because you are at the tails of the distributions. And that includes differences in spatial visualization.
On a not unrelated note, I note that nobody has commented on the over-representation of Asian employees at Google that I mentioned in the very first comment of this post and which is far greater than the male ocerrepresentation.
Apologies. I cut off my comment.
I meant to mention this paper:
So… a person is born with some level of spatial visualization skills are innate, but they could improved. And some populations work on improving those skills.
I did a quick search on “programming skills and special cognition” looking for scientific studies on the subject. I did this because the reference Mr. Kimel links to on the “Stanford” study is actually just an article (by a writer, not a scientist) with no citations referencing the source of the information provided in the article. I found that to be very strange.
If it is perceived and or argued that the capabilities of males and females in their jobs at Google are more related to programming skills and abilities, then scientific data shows that programming skills are more closely related to and mostly dependent on language skills,
Since all male v female cognitive studies have shown that females have vastly superior verbal and language skills than males on average, then the study infers and imputes that females would have better programming skills than males.
“At least for the simple code snippets presented, programmers could use existing language regions of the brain to understand code without requiring more complex mental models to be constructed and manipulated.”
“Interestingly, even though there was code that involve mathematical operations, conditionals, and loop iterations, for these particular tasks, programming had less in common with mathematics and more in common with language. Mathematical calculations typically take place in the intraparietal sulcus, mathematical reasoning in the in the right frontal pole, and logical reasoning in the left frontal pole. These area’s were not strongly activated in comprehending source code.”
The science paper from which the above statements are taken:
If you have an interest and inclination to be more informed of gender differences in different types of cognition by ages and under different conditions, this science paper is quite comprehensive and informative.
All the above citations are from 2014.
Just fyi for anybody interested in supportable facts.
“On August 10, 2017 9:40 am you said fine and good but asked if there any peer reviewed evidence that such skills affect coding ability. ”
And your post didn’t address that, as I pointed out.
“Think of it this way… say I have a group of forty year olds and a group of twenty year olds. The average 20 year old can outrun the average 50 year old in the 100M. With enough training and favorable enough conditions, that average 50 year old might manage to outrun the average 20 year old once or twice. But the best 50 year old will never outrun the best 20 year old. It cannot be done.”
True, and irrelevant. Piling up non sequiturs isn’t a rebuttal.
“On a not unrelated note, I note that nobody has commented on the over-representation of Asian employees at Google that I mentioned in the very first comment of this post and which is far greater than the male ocerrepresentation.”
And I note that you don’t comment on whether male Asian employees at Google are overrepresented compared to female Asian employees, which would have been the relevant point. Misdirection isn’t an effective tool in a scientific discussion.
I seem to be missing something. Let’s try again:
1. 2 and 3 month olds show gender related differences in spatial visualization
2. I provided the link to a peer reviewed article, as you requested, that shows spatial visualization is important to STEM careers.
3. I didn’t flat out state it, but I assumed it goes without saying that coding is a subset of STEM.
I’m not sure how we can’t conclude from this, if the sites to which I linked are correct, that there are some gender related differences in a trait that affects coding ability. What exactly am I missing?
I don’t have that information so how will I comment on it?
OK. Explain your world to me. As I understand it, in your world:
1. Women are better at coding than men
2. Coding used to be a woman’s profession
3. Something happened
4. Women have been pushed out of coding and are discriminated against in the field.
What is step 3? And why is the system perpetuated? Why would companies continue to disproportionately hire men if women are better programmers? Why does that include small startups that don’t have an established programming culture? Shouldn’t we see even a few companies go across the grain, and shouldn’t those companies that do easily beat out the companies that don’t?
You are blatantly and deliberately lying about what I posted and stated.
You are inferring what you think I think with no foundation other than by your own imagination to support it
I made no statement about what I think about whether women are better coders than males.
I posted a scientific paper on the subject of coding which said language skills are a primary skill for coders, and math centers in the brain are not significantly invoked. I said that because it is widely known by cognitive research in differences between males and females that females have much greater language and verbal skills than males and THEN it can be inferred or implied that females are better coders than males. I didn’t say anything about what I think on the subject.
I have not mentioned that females were the original coders (though it’s common knowledge among professional coders that they were).
I’ve made no statement or inferences about why females were pushed out of coding by the early 1960’s, though if you read up on the subject you’ll discover why yourself.
More-over, I have no idea about what the Google employee was referring to in the skills in which he believes women are less capable than males.
However, it is widely assumed by those with little actual knowledge of the facts that most Google employees are programmers. It is in fact the case the some sections within Google employ predominantly programmers. It is also the case that communications between programmers and non-programmers is a necessary and critical part of the business. Thus, as in any interdisciplinary endeavors it is necessary that each discipline have knowledge and understanding of the others in order to effectively communicate. This is true among chemists and physicists, biologists, electrical engineers, mechanical engineers, chemical engineers, polymer scientists, as well as the business end of financial professionals.
As far as my own direct knowledge:
I spent a career as a scientist and research engineer in high tech, and started before computers and coding were being widely used other than in very special purpose narrow endeavors. I learned to program in several languages to be able to do my math and analysis work faster with more data and analytic methods than could be done by slide-rule, pen and paper.
The essence of programming is to be able to translate one language (say math and calculus equations, or statistical equations) into a language that a) a computer can understand, and b) with knowledge of the computer’s methods in order to minimize code and maximize speed or minimize memory consumption, or minimize transfers of digital data (bits) between a storage media, cache memory and main memory, utilizing the ALU registers as efficiently as necessary considering the trade-offs between the time required to write and debug code and the computer’s speed and utilization.
Most of the modern highly utilized programming languages have been created to take most of these considerations into account “under the covers” (e.g. the compiler is optimized to handle and make the trade-offs now).
Professional computer scientists (say with a masters or doctorate in the field) aren’t coders … they direct programming or use it for their own analysis and evaluations (as a tool just like a slide-rule used to be used), or a TI or HP scientific hand calculator before computers were widely used by engineers and scientist as a tool.
So it is not at all clear to me or to anybody else either yet, what it is that the Google engineer was complaining about female skills being less than males….
– Was it coding?
– Was it in mathematics?
– Was it in physics?
– Was it in sensors technology?
– Was it in AI research?
– Was it is low level programming (real time coding)
– Was it in high level code?
– Was it in compiler architecture?
– Was it in computer systems integration?
– Was it in concepts?
– Was it in tasks?
You, nor I, have any idea, yet the entire thread is making assumptions related to “general female v male” capabilities as if all high tech endeavors require those skills for which males may on average be more proficient. not even knowing what the difference in proficiency is in any practical sense or application of a job.
BTW, you and others are clearly not even remotely aware that coding and programming require different skill sets.
In other words, Mr. Kimel, you have no idea of what your talking about., and even less knowledge of the relevant facts.
Go read this. It will give you the answer you seek. I do not wish to engage on this topic. http://washingtonmonthly.com/2017/08/11/yes-we-still-need-to-talk-about-patriarchy/
Kimel claims that some study of 2 month old babies show that spacial visualization proves that men are better at STEM careers than women.
Using Kimel’s logic let’s go back to his original posting. Since 93% of people in jail are men, it only stands to the same bogus reasoning that employers should hire women and not men.
Kimel’s logic is BS. He picks out pieces of small statistical differences that happen to support his thesis and ignores all the others. That is the definition of a bigot.
BillB and Longtooth,
1. I have stated that each individual should be judged as an individual
2 . I have also stated that the only reason to look at group averages is to reach a conclusion that if everyone given a fair shake, we shouldn’t expect the winners in every contest to be demographically representative of society.
I don’t like to link to Wikipedia, but sometimes it provides a useful summary.
Google has a competition they have run since 2003 called Google Code Jam. The list of winners (1st through 3rd place), by year, are here: https://en.wikipedia.org/wiki/Google_Code_Jam
From what I can tell, all of the first place winners are men. I spot a female name among the second place winners. This is a company that spent $150 million on diversity in 2015. How does that ratio happen?
(I note… this year Google seems to have had a separate Code Jam that was only open to women: https://sites.google.com/site/codejamtoioforwomen/home)
Facebook also runs a competition, and has since 2011: https://en.wikipedia.org/wiki/Facebook_Hacker_Cup
Facebook also spends a lot on diversity and outreach. The list of winners looks a lot like the list of winners at Google. (Some of the names are the same.) So what is going on? Why is there this disparity? You can keep criticizing me as if it is my fault if you’d like. That’s fine. I don’t much care if you blame me. But doing so doesn’t get you any closer to coming up with an explanation that both fits the known facts and that you find palatable. I would suggest you put more effort into that. You’ll get a lot more support for changing the outcomes and getting the the ratio to 50-50 if you do.
While I’m still in the mood here’s a good article that describe the facts related to male v female math proficiencies. Bottom line is that the differences are decreasing rapidly therefore cannot be attributed to biological differences.
Second, the study Mr. Kimil links to from a Stanford paper is an article not a science paper, written not by a scientist. In that article it refers to a “science” result that shows females and males have different cognition skills from early (infant) age. Although the study it refers to isn’t referenced in any way… neither by publication or date, or researcher’s names, it is very possible that it’s referring to a highly debunked and uncontrolled study that found infant females preferentially looked at faces and males looked at objects other than faces, from which many other inference and relationships were drawn.
That study was widely debunked by the research community for many solid reasons, one of which that it was a) not reproducible by the original researchers, and b) it contracted dozens of other (reproducible) similar studies, c) it was the only study with that result..
The upshot of this and my other comments is that Mr. Kimel doesn’t know what he’s talking about, and has no basis for his assertions other than his own imaginative thinking and his highly selective sources..
And he still hasn’t explained his reasoning for why he cites criminal stats for male and female incarceration rates as having any relation to male and female highly educated professional hires at Google (or any other tech company)..
You stated (812/2017, 12:10 a.m.)
” I have also stated that the only reason to look at group averages is to reach a conclusion that if everyone given a fair shake, we shouldn’t expect the winners in every contest to be demographically representative of society.
If everyone is given a fair shake why wouldn’t one expect the winners in every contest to be demographically representative of society?
You used the Google and Facebook Coding contests as evidence of your point. But those contests are open to world-wide entrants with no restrictions. The winners are from most of the advanced and highly educated nations with the US winners only barely in the running at all.
Indeed, the winners (1st through 3rd) are at the extreme upside tail of coders on the globe.. they are the +4 sigma cases A multiple time winner of many of these contests (notable on the Google Code-Jam contest is a child prodigy who won the most prestigious coding contest on the globe when he was just 11 years old (he’s now just 20)….and both his parents were well known mathematicians who “taught” him coding from a very early age. He’s from Belarus and educated there, btw.
Most winners are from nations where gender diversity isn’t much known or even tolerated, so the coding contest winners aren’t representative of Googles or Facebooks efforts to improve acceptance of diversity.
So what’s a fair shake mean to you? If you start with a stacked deck and a two-headed coin you can’t infer or invoke “a fair shake”..
Let me also point out that open entry coding contests (world-wide entrants) are also highly self selective. Only those coders who think they are capable coders enter… for two reasons:
1) To find out where they rank among the total entrants (and they all use pseudonyms with rare exceptions so their identities aren’t published or known (unless they win, place or show). This incentive to enter is for self – assessment measures….a sort of self measurement system.
2) To win, place, or show or place very high in one or more of the coding sets … especially the most difficult ones, if they are very high level coders, in order to become recognized in the coding community and for some obviously to obtain higher level employment, or entrance into prestigious universities.
Thus for the top 5% or so of coders it’s a genuine contest for money and especially bragging rights. For the remaining 95% it’s a self-assessment exercise. On a global self selective coding and programming basis, I’d ask why you think a “fair shake” includes females?
Probably the sport with the least barrier to entry is running. Just about every kid in America gets a chance to run in grade school. But US Olympians in the men;s 100 meters haven’t been demographically representative of society for at least as long as I have been alive. I have no problem with that. I think the point is to have the country’s fastest short distance runners on the team. What makes you think that the team would be better if it was demographically representative of society?
Yeah, and the OECD tracks “tertiary degrees awarded to women by field of education.” (http://www.oecd.org/gender/data/percentageoftertiaryqualificationsawardedtowomenbyfieldofeducation.htm) Latest reported figures are for 2012.
The country with the greatest percentage of women receiving advanced degrees in computer science is…. drumroll… Saudi Arabia. (44.7%)
From the bottom…. the countries that award the lowest percentage of degrees in computer science to women are… Switzerland, Belgium, Chile, the Czech Republic, the Slovak Republic, the Netherlands, Iceland, Norway, Slovenia, Poland, Austria, Brazil, France, Germany. That seems to about the lower third of the distribution. Those are, presumably, what you mean by countries where women have the least amount of freedom. I guess t a guy who thinks raping little boys is a good thing because that’s part of someone else’s culture, I guess it makes sense to start with the assumption that Saudi Arabia is a paragon of women’s freedom and Switzerland is really repressive.
But computer science hires a lot of people with math and stats backgrounds. Looking at figures for Mathematics and Statistics, women account of 75.8% of degrees in math and statistics awarded in Saudi Arabia, putting Saudi Arabia at the top in that category. The bottom is again brought up by a rogue’s gallery of women’s abusers: Switzerland (the worst offender) is followed by Columbia, the Netherlands, Chile, Norway, Iceland, France, Austria, Israel and Sweden. These repressive countries at the bottom are nightmarish places where women are forced to drive themselves places, are forced to wear what they want to wear, are forced to make their own decisions on where to travel, are forced to have freedom of religion, etc.
Saudi Arabia also has the greatest share of women (by a big margin) in what is termed “Sciences.” (69%!) which is another area that generates a lot of coders.
Which leads to the question… why are you always building your arguments on assumptions that contradict known facts? Why not look up the data occasionally rather than being consistently wrong?
On the other end of the spectrum, Saudi Arabia has the lowest share of degrees awarded to women among countries listed in Education, “Engineering, Manufacturing and Construction,” “Health and Welfare.” They also come in second to last (with a very big drop from #3) in Agriculture, but that is because Luxembourg supposedly has zero.
I have a theory about what the data is showing, but I have to mull it over.