In “Underrepresentation or Misrepresentation”, Kimura raised an opinion that the cognitive difference for men and women is caused by the prenatal levels of sex hormone. This is the most popular idea about the cognitive difference in sex and has many researches about that. However, underlying this opinion, we can find two assumptions. The first one is that there exist differences in cognitive abilities. The second is that there exist sex differences in hormones. As long as we find enough material to show that these two assumptions are true, we could make the assumption that cognitive difference is due to difference in hormone. However, with the data provided in “Science, Sex, And Good Sense: Why Women Are Underrepresented in Some Areas of Science and Math”, the two assumptions are not totally true.

There exists no cognitive difference. With the data that women are obtaining 50% of the MD degrees from medical schools and 44% of PhDs in biology and life science, we can know that women are succeeding in some sciences, which means that most of women have the cognitive abilities needed in science. Also, in general, girls get higher grades in school and are more likely to graduate from high school. In test scores, girls show great advantages in writing, which is a necessary skill in science. With those facts in mind, we may ask that if women are better than men, why most of the statistics show that women get less grades in tests. So it is proper to doubt the interpretation about existing statistics about the cognitive difference. Many people use the tail ratio (P125) to interpret those data, which shows that the ratio of males to females at extremely high-ability levels strongly favors males. However, because of fewer women in science, there must have more males than females in science who are not at high-ability levels. So if we consider about the number but not the ratio, these tests actually favor females. Also, other seemly persuasive data such as SAT-M and IQ Score Band all have their own defects, which I have showed in my previous blogs. So in fact, the existing data could not show the cognitive difference in sex but show that women have the same cognitive abilities as men.

It is true that different types of hormones predominate in different sex, though all categories of hormones are produced in males and females. So it is proper to say that there exists sex difference in hormones. However, the study of the cab drives in London (P127, Line25) shows that things inside the brain could be changed through the life with the practice. So does hormone. The fact is that hormones do not produce fixed effects and can be influenced by the environments. So because of the effects produced by hormones could be changed, we could not say that the cognitive difference is directly due to hormones. What’s more important, the cognitive differences do not exist. So the assumption made in title could never be true.

We could extend the conclusion to that all the sentences look like “Cognitive difference is caused by …” can never be true because the assumption underlying the sentences has been showed false.

So how could we explain the great sex difference in science field? If there has no cognitive difference, what is the reason for fewer women in science? Halpern raised a persuasive opinion about the jobs in science field which could answer these questions. She thought that work in science field needs a lot of time and has many pressures, so the conditions of academic life are particularly difficult for women, especially for those who have caregiving responsibilities. Here, we should distinguish her opinion with the opinion we learned before about the interest difference in science. Interest difference means that women are more willing to spend time in family rather than in science. But her opinion is quite different. She means that women have the caregiving responsibilities and have to give up the work in science. However, who said that women must have the responsibilities, which forces them to give up work in science? It is gender schema. Gender schema forces women to be nurturing and stipulates that women have the responsibilities to take care of family while men do not. If men could take responsible for part of works in family, women definitely do not need to give up their work.

Reference:

Underrepresentation or Misrepresentation   by Doreen Kimura

Science, Sex, and Good Sense: Why Women Are Underrepresented in Some Areas of Science and Math                                                                      by Diana F. Halpern

In Chapter 6, professor Lubinski and Benbow state their own opinions about why fewer women are in the science field. They use a lot of graphs to explain three main points: sex difference in cognitive ability, in interests and in willingness to work long hours. However, all the sex differences they talked about are truly caused by gender schema which they didn’t mention in their passage and the statistics in the passage cannot show the differences they talked about.

The authors use the IQ Score Band to illustrate that men’s scores have higher variability, thus causing more men at the top. However, if there are more men have higher IQ, then no matter in which field, more men than women are at the top. But in some field such as nursing and education, there are more brilliant women than men (Chapter 2). Also, the Mental Survey itself needs to be questioned, just as the SAT-M test (Chapter 4). As mentioned in Chapter 4, men and women may not have cognitive difference, but they show different aptitude for mathematics, which means women are good at solving some kinds of problems in math while men are good at others. So if the Survey is not good enough to eliminate the difference in aptitude, men and women may show difference in this test. So we cannot conclude from the IQ Score Band that men are more talented in science.

In order to illustrate that there are more men in science field, the professors show that people who are more gifted in science are more likely to study in science. This statement is based on the previous conclusion that men are more talented in science, which has been proved deficient, so the statement is not proper. The true reason that leads to more men in science is gender schema. From Chapter 1, girls who know their gender are more likely to perform badly than girls who do not recognize their gender. In Chapter 6, from the two graphs about the favorite high school and college courses, we can see that as the age increasing, the percentage of women in math and science is decreasing rapidly, which shows that girls are more affected by the gender schema when they realizing it as they grown up.

The authors state that women and men have sex difference in interests. In fact, women are not truly less interest but are influenced by gender schema. In Chapter 4, the knowledge of a person’s gender influences faculty members’ assessment of women. When judging women’s honors, people always think that women don’t pay as many efforts as men, and even think that women don’t do the work by themselves (Steinpreis, Anders and Ritzke, 1999). Because of this bias which means that they have to pay more efforts to overcome the accumulated disadvantages, fewer women choose to work in science and math. So the gender schema forces them to choose jobs outside of science, and this has no relation about interests. In Chapter 6, the study made by Webb, Lubinski, and Benbow (2002), which the authors use to prove the sex difference in interests, is a great example of my point.

The authors talk about the difference in willingness to work long hours and show that women are more willing to work less. This is, in fact, caused by the gender schema. As in Chapter 4, men and women may have equal desires for work, but they face unequal chances and difficulties of realizing both desires. This discourages them from choosing careers in math and science. In the picture 1 of Figure 6.5 in passage, we can see that the typical work hours for women are less than men, which definitely shows the gender schema in workplace. So the less willing to work long hours showed in picture 2 is truly caused by the gender schema in workplace but not the sex difference in willingness to work long.

Although the authors believe that sex difference causes an overrepresentation of males in STEM fields, I believe, the true reason is gender schema. We cannot conclude that there is no sex difference in science field, but before we talks about the sex differences which are innate, we should eliminate the outer reason—the gender schema.

Reference:

Why Aren’t More Women in Science? (Chapter 1 to Chapter 6) 

                                                by Stephen J. Ceci and Wendy M. Williams

In Chapter 5 “Taking science seriously: straight thinking about spatial sex difference”, professor Newcombe uses a lot of statistics to show her points about spatial sex difference. She talks about three main points in the passage: there is no biological explanation for the spatial sex difference and the spatial ability can be improved, and then the sex difference in spatial abilities is hard to be eliminated. This passage is very well-organized and based on lots of convincing statistics. But some points in the passage still need to be argued.

After reading the whole passage, I really get confused about the main points in the passage. If the sex difference is not caused by biological difference and we can improve it by effort, we can definitely get convergence in this ability. But from her words, we can see that the ability difference is hard to be eliminated. So there must have some other reasons and causes which are not based on biology to the spatial difference in sex. However, she just talks about the facts she found from the statistics but doesn’t show any thoughts about the other reasons which caused this sex difference. I think this is a very important point she should add into her passage to make the passage more logical.

In the first paragraph of the passage, she uses the example that the average American man has an ability to perform mental rotation of a three-dimensional object that exceeds that of the average American woman by half a standard deviation or more in order to show that men have strengths in the spatial domain. This is not a good example because its lack of convincing. It is very hard to test the mental rotation of a three-dimensional because all the things are happened inside the brains. And how could we compare this ability? The author should add more details about this experiment to make her argument more convincing.

In her first main point about the current evidence on causation, she argues that the thoughts that the earlier in development are effect to observed, the more likely it is to be biological caused are wrong. So she has a totally difference opinion with the author in Chapter 4 about this point. In Chapter 4, the author proved that because in experiments, children shows no difference in spatial abilities, men and women have no difference in spatial abilities. But in Chapter 5, Newcombe believes that early-emerging effects can be environmentally produced, which contradicts her pervious points. Also, after few sentences about this point, she turns to the point that sex difference can be partially explained by access to school. In fact, I think she doesn’t fully explore her points and turns to a seemly less relevant point quickly, which makes her argument confusing.

As I mentioned above, there must have the other reasons which caused this sex difference. And what I think is the gender schema. From the gender schema, a girl must be expressive and nurturing, but have less ability in reasoning thinking. So if a girl gets high education in the sciences, people around her may think that she is not a “girl”. And we can see the statistics from passage one that if women who at first do better than men recognize their gender schema, they will not do better anymore. So gender schema may explain that the question that why girls cannot catch up boys even when they have no biological difference and when the spatial ability can be improved by effort.

Last but not least, the author has done a good job that makes the passage more attractive. She describes some figures of fun to begin the passage, and then lead to the arguments. Also, she talks about her main points briefly before the truly discussion, which makes the construction of passage more obvious. And at the end of the passage, she points out that we would do better to concentrate on understanding how to educate for spatial skills rather than focus solely on the explanation of sex difference, which is a very good point. From all these five passage, we only talk about the explanations of sex difference, but I think, the action is more important.

Reference:

Women at the Top in Science—and Elsewhere           By  Virginia Valian

Sex, Math and Science                           By Elizabeth S. Spelke and Ariel D. Grace

Taking Science Seriously: Straight Thinking About Spatial Sex Difference   By Nora S. Newcombe

In “Sex, Math and Science”, the professors Spelke and Grace have proven that women are influenced by the discrimination in math and science field, which leads to fewer women in science. In fact, this passage uses the similar way in passage one, which is that raising the prevalent opinions about the questions and then uses the data and experiments to discuss the opinions. This is definitely a good way to prove things.

Here comes a question: is there any difference in cognitive abilities between men and women? From the passage we can see that the author uses great experiments about the infants to show that there has no primary difference between male and female babies. And also there has a great variability when they grow up. This definitely shows that when we were born, we were the same in cognitive abilities. So there must have another factor which comes after our births to explain for the great variability. What is the factor? It’s the discrimination caused by the gender schema. From the three examples from parents we can see that the gender label deeply influences the parents and forces them to believe that men are stronger and more gifted at math and science, though it is not true. So the gender label passes to the children’s minds, thus influencing them unconsciously. This passed gender label influences generation by generation, making the whole society to think like that. Also, the knowledge of a person’s gender influences faculty members’ assessment of women. When judging women’s honors, people always think that women don’t pay as many efforts as men, and even think that women don’t do the work by themselves when the work is brilliant. Because of this bias which means that they have to pay more efforts to overcome the accumulated disadvantages, fewer women choose to work in science and math. So the truly reason is not the cognitive differences but the gender discrimination.

Although I believe that men and women have no cognitive difference, they are definitely good at different aspects of cognitive abilities. Women are better in verbal problems, while men are better in spatial problems. And the reason for this difference, I think, is the difference in sex hormone mentioned in passage two, since from recent researches, prenatal androgen levels are almost certainly a major factor in the level of adult spatial ability. But the difference matters nothing about the whole cognitive difference and the grades in math and science, because in math and science, both verbal and spatial abilities are very important in solving problems. There is nothing to say the one is better than the other. What’s more, these two abilities can be trained through efforts. Because these two abilities are both important in math, the brilliant scientists in math must have both abilities. So if women are born with better in verbal problems, they can practice to do spatial problems well.

Is there any difference in motivation? It’s true that women are more likely to balance the work and family, but this is not the reason. In Chapter one, we can see that women with children who have to pay more attention to families have published as many professional articles in math and science as those women without children. The truly reason is the discrimination. Men and women may have equal desires for work and family, but they fact unequal chances and difficulties of realizing both desires. This definitely discourages them from choosing math and science.

Passage one, two and four all mention the statistics from SAT-M, but is it a useful data to prove something? From the SAT-M, we can see that male get higher scores on the exam, and in passage two, this leads to the conclusion that women and men have cognitive difference. But the statistic itself needs to be questioned. First, we don’t know the proportion of men and women who get high scores. Maybe the attendance of women is less than that of men due to the gender schema, which makes the number of women in high scores less than men. Second, as I mentioned above, women are better in solving verbal problems than solving spatial problems. So if the distribution of questions in the exam is that the number of the verbal problems is less than the number of the spatial problems, this will definitely show lower grades in women. But this doesn’t show the cognitive difference.

Do you know who build up the system of math? I don’t know the exact person, but the whole groups of people who build up the system of math are definitely men. This is because at that time, women are not allowed to get access to higher education and then cannot take part in the work. Because math is build by men, there may have something that prefers men, though we don’t know. So what will happen if we break the system of math but build another system such as hexadecimal system? Maybe more women scientists will emerge in this system.

All in all, there are so many outer reasons, so it’s impossible to say that men and women have innate differences such as cognitive difference before we keep away all the outer perspectives.

Reference:

Women at the Top in Science–and Elsewhere   by Virgnia Valina

Underrepresentation or Misrepresentation     by Doreen Kimura

Sex, Math and Science              by Elizabeth S. Spelke and Ariel D. Grace

Although this is not a comparative essay, I think that this passage “Is Math A Gift? Beliefs That Put Female at risk” raises lots of different opinions about fewer women in top science compared with passage one and two. Carol thinks that men and women have no difference in math ability, but women who believe that math is a gift are more vulnerable when facing challenges.

Compared with gender schema and cognitive difference, the vulnerability difference between men and women is a point that not easy to think about, but the statistics and experiments made by the author prove it. And who makes this vulnerability? It’s the belief that math is a gift. Girls who believe math is a gift will be suspicious about their abilities when facing difficulties in math, while girls who believe that math can be learned through effort will not be so vulnerable. In fact, when I first see the question “Is math a gift?”, I just think this is definitely an excuse. It is an excuse for your less effort. I know that many girls have these thoughts that math is a subject for who has the math talent. This kind of belief will make you to pay less effort to that because you think you can’t do it well due to the less talent. This definitely will lead to bad expressions in math. In fact, as an advocate for incremental theory, I totally believe that math can be improved by effort. From Chapter 1 we can see that there is no talent in human beings that can be assessed. And even if there has this kind of talent difference between men and women, it can be improved by effort. In fact, the ratio difference at top is notably decreasing, and at the medium grades, women do better than men. So if the statement that math is a gift is true, this talent loves women.

But here comes a question: why do men get less influence from this belief compared with women? From the statistics we can see that men are less influenced by the belief and are less vulnerable. But the author points out that this vulnerability is caused by the belief that math is a gift. And there must be some boys who also think that math is a gift. So why don’t the boys get the influence and become vulnerable? From the argument in the passage, we cannot see that. So this is a very important shortage in the passage that we should to find out. And what I think is that it’s because gender schema. The gender schema told girls that you are not good at math. So those who believe math is a gift will be influenced more by the stereotype and thus influence the scores. However, for boys, the gender schema told them that they must be good at math. So the gender schema forces them to pay more effort and can do well. This is only an assumption, and at this time, I don’t have many statistics to support it. So what I do is just to let my readers to think about this question.

The professor also talks about the ways to reduce this vulnerability, and she thinks that what we should do is to change the girls’ opinions that math is a gift. She denies the commonsense way—praising girls when they do good jobs. She thinks that this will convey them the opinion that math is a gift, and when they find they are not the gifted ones, they will lose confidence. This way sometimes will lead to the situation that the author depicts, but this way is not as bad as that. From my personal view, I think praising is a very good way but you need to care about the words in praising. If you always use the words like “you are so talented and gifted”when you are praising someone, this will lead to the bad situation. But if you praise someone like “you did a very good job because of your hard working”, the situation may be a good one. And these words can help people to be more hard-working and believe that math is something that we can get through efforts and thus change the suspicious girls’ opinions. Also, the professor uses the experiments to points out that we can address the students’ belief about nature of ability by teaching them and making lectures about the topics and then improve their performance on math. However, what I think is that the belief which is caused by gender schema cannot be moved so easily. This kind of gender schema has influenced us unconsciously for over than 15 years, so how could it change in only eight-session intervention? So I think that the “showed” increase in grades for girls after the intervention is not a representative data which leads to the conclusion.

What’s more, I really doubt the process of the experiments mentioned in this passage. First, we can see that the experiment for proving that changing the students views can lead to a great increase in grades has more than one variable. The one is learning about the expandable nature of intellectual skills, and the other one is learning high-quality instruction in useful skills. So with two variables, we cannot get such a causal conclusion. Second, for another experiment mentioned in the passage which is teaching adolescents the same math lesson in two different ways, one group is taught that math is a gift, which reinforces their opinions from the gender schema. This is definitely not fair for the students in this group.

So after reading the passage, I think that the belief that math is gifted is definitely a reason for fewer women in top science, but the passage has not proved it perfectly.

Reference: Is Math a Gift? Beliefs That Put Females At Risk ———Carol S. Dweck