Write Me Women And Gender Studies Literature Review - Opinion of professionals

rides areGrant says that gender differences are small and irrelevant to the current issue.

As a social scientist, I prefer to look at the evidence. The gold standard is a meta-analysis: There are only a handful of areas with large sex differences: Suppose I wanted to convince you that men and women had physically identical bodies. Http://cocktail24.info/blog/best-letter-ghostwriting-website-for-phd.php run studies on things like number of arms, number of kidneys, size of the pancreas, caliber of the aorta, whether the brain is in the head or the chest, et cetera.

I conclude that men and women are mostly physically similar. I sure showed you, you sexist! And in fact, Hyde found that men were indeed definitely more aggressive, and women indeed definitely more sensitive. Perhaps some peeople might think that finding moderate-to-large-differences in mechanical abilities, computer skills, etc supports the idea that gender differences might play a role in gender balance in the tech industry.

Or possibly not, see here ]. The study very specifically says the opposite of this. Its three different numbers for physical aggression from three different studies are 0. On the Write Me Women And Gender Studies Literature Review hand, Grant fails to report an effect that actually is large: So Grant tries to argue against large thing-oriented vs.

Next, Grant claims that there are no sex differences in mathematical ability, and also that the sex differences in mathematical ability are culturally determined. Grant says that these foreign differences in math ability exist but are due to stereotypes, and so are less noticeable in more progressive, gender-equitable nations:. Girls do as well as boys—or slightly better—in math in elementary, but boys have an edge by high school. Male advantages are more likely to exist in countries that lack gender equity in school enrollment, women in research jobs, and women in parliament—and that have stereotypes associating science with males.

But I want to go back to the original question: Is this also due to stereotypes and the effect of an insufficiently gender-equitable society? Galpin investigated the percent of women in computer classes all around the world. The most sexist countries do extremely well on this metric!

The highest numbers on the chart are all from non-Western, non-First-World countries that do middling-to-poor on the Gender Development Index: Needless to say, Zimbabwe is not exactly famous for its deep commitment to gender equality. Previous research suggested that sex differences in personality traits are larger in prosperous, healthy, and egalitarian cultures in which women have more opportunities equal with those of men.

The countries with the lowest sex differences are Indonesia, Fiji, and the Congo. I conclude that whatever gender-equality-stereotype-related differences Grant has found in the nonexistent math ability difference between men and women, they are more than swamped by the large opposite effects in gender differences in personality.

We know that interests are highly malleable. Female students become significantly more interested in science careers after having a teacher who discusses the problem of underrepresentation. In exchange, they get constant glowing praise from every newspaper in the country 12345678910etc, etc, etc. The graph that Grant himself cites just above this statement shows that, over the click the following article ten year period, percent women CS graduates has declined nationwide.

Do you think no one else has tried? Some further discussion by Mudd Write Me Women And Gender Studies Literature Review in the comments here ]. The data on occupational interests do reveal strong male preferences for working with things and strong female preferences for working with people. But they also reveal that men and women are equally interested in working with data. So why are there so many more male than female engineers?

Because women have systematically been discouraged from working with computers. Look at trends in college majors: In the yearwomen were locked out of almost every major field, with a few exceptions like nursing and teaching.

The average man of the day would have been equally confident that women were unfit for law, unfit for medicine, unfit for mathematics, unfit for linguistics, unfit for engineering, unfit for journalism, unfit for psychology, and unfit for biology.

As the feminist movement gradually took hold, women conquered one of these fields after another.

This makes no sense. There were negative stereotypes about everything! Somebody has to explain why the equal and greater negative stereotypes against women in law, medicine, etc were completely powerless, yet for some reason the negative stereotypes in engineering were the ones that took hold and prevented women from succeeding there.

Intermittent fasting women may experience benefits that include better chances at fighting cancer, diabetes, & autoimmunity. This comes with a HUGE caveat. “The conceptual penis as a social construct:” a Sokal-style hoax on gender studies by @peterboghossian and @GodDoesnt. ABOUT US. We value excellent academic writing and strive to provide outstanding essay writing services each and every time you place an order. We write essays. ClassZone Book Finder. Follow these simple steps to find online resources for your book. Our analysis shows that, for grades 2 to 11, the general population no longer shows a gender difference in math skills, consistent with the gender similarities.

And if your answer is just going to be that apparently the negative stereotypes in engineering were stronger than the negative stereotypes about everything else, why would that be? Put yourself in the shoes of our Victorian sexist, trying to maintain his male privilege. And if I had to, I would accept women going into law and determining who goes free and who goes to jail. But women building bridges?

This is the best explanation the world can come up with? The same patterns apply through pretty much every First World country, and if it were just a matter of personalities you would expect them to differ from place to place. But I notice that doctors and lawyers are also pretty high-paying, high-status jobs, and that nothing of the sort happened there.

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Note that many of these imbalances are even more lopsided than the imbalance favoring men in technology, and that many of these jobs earn much more than the average programmer. But once you take off the blinders and try to look at an even slightly bigger picture, you start wondering why veterinarians, who make even more money than that, are even more lopsidedly female than programmers are male.

Does it happen at the college level? So differences exist before the college level, and nothing that happens at the college level — no discriminatory professors, no sexist classmates — change the numbers at all. Does it happen at the high school level? There are no prerequisites except basic mathematical competency or other open-access courses.

So something produces these differences very early read more

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What might that be? On a scale ofwhere 5 represents complete certainty in gender equality in computer skills, and 1 completely certainty in inequality, the average woman chooses 4.

This seems to have been true since the very beginning of the age of personal computers: Undergraduate mathematics itself more-or-less shows gender parity. Might sexist parents be buying computers for their sons link not their daughters, giving boys a leg up in learning computer skills?

In the 80s and 90s, everybody was certain that this was the cause of the gap. Newspapers would tell lurid and entirely hypothetical stories of girls sitting down to use a computer when suddenly a boy would show up, push her away, and demand it all to himself.

But move forward a few decades and now young girls are more likely to own computers than young boys — with little change in the high school computer interest numbers. One subgroup of women does not display these gender differences at any age. These are women with congenital adrenal hyperplasia, a condition that gives them a more typically-male hormone balance. For a good review, see Gendered Occupational Interests: Consistent with hormone effects on interests, females with CAH are considerably more interested Article source are females without CAH in male-typed toys, leisure activities, and occupations, from childhood through adulthood reviewed in Blakemore et al.

Male-typed interests of females with CAH are associated with degree of androgen exposure, which can be inferred from genotype or disease characteristics Berenbaum et al. Interests of males with CAH are similar to those of males without CAH because both are exposed to high sex-typical prenatal androgens and are reared as boys.

Females with CAH do not provide a perfect test of androgen effects on gendered characteristics because they differ from females without CAH in other ways; most notably they have masculinized genitalia that might affect their socialization. Further, some findings from females with CAH have been confirmed in typical individuals whose postnatal behavior has been associated with prenatal hormone levels measured in amniotic fluid.

Amniotic testosterone levels were found to correlate positively with parent-reported male-typed play in girls and boys at ages 6 to 10 years Auyeung et al.

The psychological mechanism through which androgen affects interests has not been well-investigated, but there is some consensus that sex differences in interests reflect an orientation toward people versus things Diekman et al. The Things-People distinction is, in fact, the major conceptual dimension underlying the measurement of the most widely-used model of occupational interests Holland, ; Prediger, ; it has also been used to represent leisure interests Kerby and Ragan, and personality Lippa, In their own study, they compare such women and find a Things-People effect size of The results support the hypothesis that sex differences in occupational interests are due, in part, to prenatal androgen influences on differential orientation to objects versus people.

Compared to unaffected females, females with CAH reported more interest in occupations related to Things versus People, and relative positioning on this interest dimension was substantially related to amount of prenatal androgen exposure.

So this theory predicts that men will learn more here more likely to choose jobs with objects, machines, systems, and danger; women will be more likely to choose jobs with people, talking, helping, children, and animals. Somebody armed with this theory could pretty well pretty well predict that women would be interested in going into medicine and law, since both of them involve people, talking, and helping.

They would predict that women would dominate veterinary medicine animals, helpingpsychology people, talking, helping, sometimes childrenand education people, children, helping.

Of all the hard sciences, they might expect women to prefer biology animals. And they might expect men to do best in engineering objects, machines, abstract systems, sometimes danger and computer science machines, abstract systems. There are wide differences in doctor gender by medical specialty. Meanwhile, Radiology is machines and no patient contact, Anaesthesiology learn more here also machines and no patient contact, Emergency Medicine is danger, and Surgery is machines, danger, and no patient contact.

This should be shocking. I was totally confused by this for a while until a commenter directed me to the data on what people actually do with math degrees. The answer is mostly: They work in elementary schools and high schools, with people. Write Me Women And Gender Studies Literature Review all those future math teachers leave for the schools after undergrad, and so math grad school ends up with pretty much the same male-tilted gender balance as CS, physics, and engineering grad school.

Figure out a way to make math people-oriented, and women flock to it. If there were as many elementary school computer science teachers as there are math teachers, gender balance there would equalize without any other effort.

And so on for most other fields. This theory gives everyone what they want. It explains the data about women in tech. It explains the time course around women in tech.

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