The Myth of White Male Geek Rationality

People who consider themselves fully rational individuals are ignorant about basic psychology and their own minds.

It is easy for white men in science, technology, engineering, and math (STEM) fields to perceive themselves as more rational than other groups, because our society associates rationality with whites, men, and STEM professionals. When white men in STEM fields believe in this stereotype, they might assume that bias is more common in non-white people, women, and people in the arts, humanities, and social sciences. After all, these other groups seem to want to discuss bias more often, and unexamined associative “reasoning” would link bias to those who bring up the topic of bias. Under logical scrutiny, however, it does not follow that the act of thinking about bias makes one more biased.

Green Red Blue
Purple Blue Purple


Blue Purple Red
Green Purple Green


the Stroop effect refers to the fact that naming the color of the first set of words is easier and quicker than the second.

A basic tenet of contemporary psychology is that mental activity can be unconscious. Unconscious simply refers to any mental activity that is “not conscious”, and it is not equivalent to the unscientific New Age concept of the Subconscious. A good example of unconscious mental activity interfering with conscious intentions is the Stroop effect (right). If you try to name the colours of the colour words aloud, the first set of colours will be easier to name than the second set of colours, because you unconsciously read the words. This means that you do not have full control over your thoughts and behaviour, and your willpower or logical reasoning cannot overcome the unconscious cultural bias of being able to read in English. Of course, there are other unconscious cultural biases aside from English literacy bias.

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Discussing sexism in geek communities is more important than discussing gender imbalance.

Some female geeks use the discourse of increasing female representation in science, technology, engineering, and math (the “STEM” fields) as a proxy for addressing sexism in geek communities. Because countering sexism against women does not directly benefit men, some women reframe the issue of sexism by appealing to capitalist values. They argue that if women are better represented in STEM fields, it would lead to economic growth and technological innovation (and that this can be achieved through efforts to reduce gender bias).

However, this strategy backfires when male geeks interpret the movement to increase female representation in STEM fields as “social engineering”, i.e., feminists forcing women to do what we purportedly “dislike” (science, tech, engineering, and math). The subtext of this movement—which is that female geeks who love STEM topics have to endure sexism from male geeks or get out, and this is a Bad ThingTM that needs to be fixed—is lost entirely.

Observe this Digg comment on the Bias Called Persistent Hurdle for Women in Sciences submission:

''There is nothing more miserable than a career that you don't really enjoy. But don't let that stop feminists from pushing other women into jobs they won't like. They have an agenda and ***** up someone else's life is not a consideration.'' (+10)

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Environmental and social barriers restrict women in science, tech, engineering, and math.

Why So Few? Women in Science, Technology, Engineering, and Mathematics (PDF) is a new, publicly-accessible research report by AAUW that “presents in-depth yet accessible profiles of eight key research findings that point to environmental and social barriers – including stereotypes, gender bias and the climate of science and engineering departments in colleges and universities – that continue to block women’s participation and progress in science, technology, engineering, and math.”

The report is quite comprehensive, and summarizes and integrates studies from different research areas. At the end of each chapter are practical recommendations based on research findings. Here is a list of the detailed chapters: Chapter 1: Women and Girls in Science, Technology, Engineering, and Mathematics; Chapter 2: Beliefs about Intelligence; Chapter 3: Stereotypes; Chapter 4: Self-Assessment; Chapter 5: Spatial Skills; Chapter 6: The College Student Experience; Chapter 7: University and College Faculty; Chapter 8: Implicit Bias; Chapter 9: Workplace Bias; Chapter 10: Recommendations.

Commentary in the blogosphere:

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