Sex, Math, and Science – Blog Post 4 Rachel Tang

The most famous and controversial opinion of why there are more men than women in the STEM field is from Harvard University’s former president, Lawrence Summers. Elizabeth S. Spelke and Ariel D. Grace’s essay “Sex, Math, and Science” explores the three reasons Summers listed for the reason for the gender gap in the STEM field and proves why Summers’ arguments are flawed. They then go on to prove how men and women are stereotyped from a young age, which in turn deters women from entering the science and math field, as well as the distorted perception of how women are treated in the STEM field.

Spelke and Grace focuses on tackling popular claims society has made about men having more skill in math and science than women do, starting off with the claim that men tend to learn more about mechanical relationships while women tend to learn more about human relationships between people, therefore men tend to follow their stronger skill and go into math and science. However, Spelke and Grace proves that this statement does not hold true. At infancy, male and female toddlers learn the mechanics of objects at the same rate, with girls learning earlier than boys the correlation between the forces exerted on an object and the distance of the object. The same research is proven to be true throughout development. I would agree with the research that Spelke and Grace used because girls and boys are taught the same things from a young age and tend to do the same things, therefore neither sex is given an upper hand at a certain skill.

Retrieved from: ibtimes.com

 

Spelke and Grace also bring in research on how men and women use different strategies to go about the same math problem, which means that they tend to have different speeds for tackling different types of question. They then go on to prove how many standardized tests, like that of the SAT-M test, used as a yardstick to measure men and women’s aptitude in math is inaccurate and gives men an advantage over women. If given the assumption that the SAT-M test was questions taken that were most similar to those found in the mathematics field or most similar to tasks companies would need people to do, then I would disagree with what Spelke and Grace have found. From an economics point of view, a company would hire the person who is most efficient. If the questions from the SAT-M reflect well upon the questions that students would need to know for that line of work, and men tend to test better in it just because their strategies are faster for those types of questions, companies would hire men over women who tested better because time is an important and limited resource. However, if the SAT-M test questions were not an accurate representation to that of questions found in the real world, I would be inclined to agree with Spelke and Grace on the tests being the problem, as then it would benefit men more than women while still having questions that are inacurrate representations of what is expected to be known in the workplace.

When giving their own solution, Spelke and Grace believes that the problem is in parents’ gender bias and how they raise their children with certain stereotypes they believe are true in certain genders. From the research they have concluded, Spelke and Grace make the assumption the parents tend to overestimate their sons while underestimating their daughters. While unsure of whether or not this assumption is true, if it is, I would agree that this causes a hindrance in development in the math and science field because parents who overestimate their children tend to encourage them to do more, while parents who underestimate their children to do discourage them to do more or to do something that seems to be hard, such as working in the STEM field.

Spelke and Grace bring in many new points. While unsure of whether or not I am in complete agreement with the arguments due to the overwhelming amount of research but lack of depth when describing them, I believe that they have brought up good points that society must take a look at.

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