What if I asked about your attitude toward Lawrence Summer’s address that women’s innate reasons cause their absence in science? Well, the issue has been debated again and again that even for the same argument, there are lots of different explanations. Sex, math, and science, an academic response of Lawrence Summer’s assertion, points out Spelke’s belief that it is not cognitive difference but social discrimination causes women’s absence in science. Her statements that gender schema and discrimination are reasons for the phenomenon are very convincing, but part of her statements lack logic and can be questioned.

Spelke’s points about gender discrimination and social elements are very convincing. I believe that women’s underrepresentation due to discrimination decrease science’s attractiveness to young female. And I believe that Spelke’s statement about women’s plausible low interest in science: it is not women’s lack of interest but more the invisible cost women need to pay to pursue a scientific occupation that stops women’s pavement to science career. She points out that children will be treated differently because of their gender, as parents always see boys as stronger, more intelligent, and aggressive. People’s assessment about candidates’ competence is different towards male and female, in which people tend to believe males have more ability and tend to doubt females that show extreme competence in their resume. Spelke’s data convinces me. It reminds me about social schema theory and several similar experiments. First is about judgment of ability of different genders. When men and women have similar resume, people tend to see male candidates as more competent. And when female candidate are especially competent, people admit her ability but describe her as more hostile (Heilman, Wallen, Fuchs, Tamkins, 2004). The second experiment shows people’s standard shift: there are two candidates that either have more education history or more work experience. When the former is male and latter is female, people tend to choose the former. But when their gender switched, people change their standard to support the male (Norton, Wandello, 2004). Furthermore, even females tend to underestimate female themselves. Both experiments convince me about the existence of the influence of social stereotype to women. In many situations, becoming a scientist means that females have to tolerate extra pressure and bias, discrimination of workplace and extra difficulty to advance. In China, there is pressure of marriage and advancement difficulties for female scientists, stemming from people’s biased attitude and negative description on media. In three top universities ( Beijing University, Tsinghua University, GUCAS), eighty percent of women doctoral students doesn’t have relationship (Tingru Li, 2008).

Though I agree with Skelke’s viewpoint, her rejection towards sex difference in cognitive ability has many logic loopholes. She rejects Summer’s three factors that women focus more on people, women are less talented and women are less motivated. I think her logic isn’t convincing. The first factor is that men are more focused on objects, and girls on people. Without a definition of “focus on object” and “focus on people”, Spelke compares the time male and female infants need to study discerning the distance an object moves and study how to build stacks. Depends on equal time period infants need, she infers that infants of different genders have same abilities to learn about objects, thus there are no different focuses for males and females. However, Spelke’s assumption is that similar time to learn something means equal focuses and interest on such thing. I don’t think so, as interest does not equal ability. One may has ability to do one thing but doesn’t have interest in it; one can also lack the ability to do a thing but has enthusiasm about it.

The second factor is the different mathematic and spatial ability of men and women. Spelke compromises that adult of different genders have different favored types of mathematics abilities, which may be a key variable in high-level mathematics. She makes a great point that the SAT test doesn’t have equal items that favors men and women, so the SAT data cannot reflect aptitude of women and men. Spelke’s assumption is that mathematical abilities that favor male and female are equally important in high level math. However, what if the distribution of mathematic ability doesn’t equal the abilities needed in the high level? What if some kinds of abilities are especially important in the high level math that favors certain gender? Other study implies that the ability of spatial and rotation, which favors male, may be the key ability to achieve high-level success in mathematics.

Moreover, the following experiment about effectiveness of studying mathematical materials of male and female students has lots of uncontrolled variables. Spelke’s first assumption is that amount of males and females taking math course and their grades can reflect math aptitude of different genders. But I doubt it. People will find it farfetched to link a student’s class choice with his or her aptitude. If a student’s attendance of mathematic class proves his or her high aptitude, the student doesn’t need to study hard to prove it anymore. We can just choose four most difficult mathematical courses and then we can convince that we are geniuses. As for grades, there are more than one variable. What if one student studies very hard to get certain grade and another one just feels easy to get the same grade? Do they have the same aptitude? I don’t think so. As there are many extraneous variables, the validity of such study can be questioned. The study may not show a suitable causal variable of aptitude but may just reveal a correlation. In addition, there are other research suggests that men have higher aptitude. In such research, men and women are asked to solve questions that were not already rehearsed in a classroom, and males get better scores (Felson & Trudeau, 1991; Wainer & Steinberg, 1992).

In her rejection to the third factor that there are more male geniuses, she equates some objects that have different definitions. She uses the male-female ratio of mathematics major to measure the geniuses in each field, which reveals equal numbers of male and female in this major. So she concludes that there are not more male geniuses. However, her assumption, again, shows simplification that composition of genders reveals the composition of geniuses of different genders. For example, there may be ten percent of male students have high talent but only five percent of female students have such talent. The geniuses are people have high mathematics abilities, and they don’t have causal relationship with major choices. There is also opposite research shows that there are especially large difference in tail of intelligence distribution in one percent, five percent and ten percent of different genders. And males excel over females ( Hedges& Nowell, 1995).

As conclusion, though Spelke’s inference about sex difference of cognitive ability has some loopholes, her points about gender bias and discriminations convince me. Again, back to Summer’s assertion, the debate of cognitive difference of males and females still need further evidence. I hope new research will reveal deeper reality.

Citation:

Hedges, Nowell, Sex differences in mental test scores, variability, and numbers of high-scoring individuals.

Doreen Kimura, Underrepresentation or misrepresentation?

Tingru Li, Walking along in the edge between male and female—female doctorate “ third gender” phenomenon analysis.

Virginia Valian, Women at the top in science——and elsewhere.