This guest post is by computational physicist Jonah Miller, who interviews his mother, Dr Arleen Miller, about her experiences getting a STEM degree in the 1970s. Her dissertation was focused on mathematical outcomes of girls and boys. She also shares experiences teaching mathematics in Sierra Leone.
January 6th is my mother’s birthday. As a present, I decided to showcase the first scientist I ever knew—one who I met before I was even born.
Arleen Garfinkle (one day to be Arleen Miller) entered graduate school at the University of Colorado in the fall of 1973 and graduated in 1979. During that time she developed a battery of tests designed to track a child’s numerical and logical reasoning skills, based on the theories of psychologist Jean Piaget.
Once she developed the test, she gave it (and several other tests) to over 200 pairs of twins aged four through eight and correlated their success rates to other factors, such as their gender and how much their parents emphasized success. One of her most significant findings was that a young child’s ability to learn math was highly dependent on genetics. Another was that gender had no effect on performance—i.e., girls and boys were equally good at math.
Despite being offered a prestigious position at Yale University, my mother left academia to pursue other interests. But to me, she’ll always be my favorite scientist. (more…)
Here is an examination of the scientific flaws in the recent New York Times (NYT) Op-Ed: “Academic Science Isn’t Sexist.” The Op-Ed authors, psychologists Professor Wendy Williams and Professor Stephen Ceci, put forward various wide-sweeping statements about the effect of gender on academic careers of women scientists. The article outlines the fact that women make up a minority of junior faculty members, particularly in maths-intensive fields like engineering and computer science (25%-30%) and an even smaller proportion in senior positions (7%-15%).
Williams and Ceci argue that much of the empirical studies that established gender inequality in academia are outdated (mostly published prior to the year 2000). They argue that more recent data show that inequality has been diminished in academia. The researchers claim that women are promoted and remunerated at the same rate as men – except in economics. Williams and Ceci further argue that women’s numbers have been steadily growing in the life sciences and psychology. They note that the proportion of women in maths-intensive fields has also been growing, but not as much. Their analysis attempts to explain why this is the case.
The central argument presented in their NYT article is that women would fare well in maths-intensive subjects, “if they choose to enter these fields in the first place.” To put it another way, the problem as they see it, is that gender inequality is a myth, and that the discrepancies between men and women would be reduced if women chose to stay in STEM.
The Op-Ed is based on the co-authors’ study published in November in the journal, Psychological Science in the Public Interest. In their study, Ceci is first author and they are joined by two economists, Professor Donna Ginther and Professor Shulamit Kahn. The research team see that the sex variations within the fields of Science, Technology, Engineering and Mathematics (STEM) represent a “contradiction” and a “paradox.” The logic of their argument is that because there are more women in STEM fields today in comparison to the 1970s, and because there are different patterns of attrition amongst various disciplines, this is evidence that sexism in academia is a moot point. The crux of their argument is simple: if there are differences between men and women’s career trajectories in STEM, these arise from personal preferences, and not due to a culture of sexism.
The are several problems with the Op-Ed, which overly simplifies the body of literature the authors reviewed, but the analysis of study itself is highly flawed. The most glaring issues include the concepts used, such as the authors’ confusion of sex and gender and how these relate to inequality. Another set of problems arise from the authors’ methods. Put simply: the way they measure gender inequality does not match the data they have available, and their interpretation and conclusions of the data are therefore invalid. In science, a study can be seen to be valid when the phenomenon measured matches the instruments used. The concepts, data collection and analysis need to match the authors’ research questions. This is not the case with this study.
Let’s start with the key concept the authors measured: gender inequality, which is also discussed as “academic sexism.”