Why Social Scientists Should Not Participate in the March for Science
Many social scientists are excited about and poised to participate in the upcoming March for Science, which is being described by the organizers as a “celebration of our passion for science and a call to support and safeguard the scientific community.” I realize that this will be a controversial position, but I believe the best way social scientists can contribute to the March for Science is to quietly sit this one out. I am very much pro-science and share some of the concerns people have about cultural and political threats to science. That being said, in my opinion, the social sciences are currently too compromised to help the cause. Even those who have the best intentions risk doing more harm than good.
Why? For one, there is very little political and ideological diversity in the social sciences. It is true that many academic fields lean left, but this especially the case within the social sciences. Check out Heterodox Academy for details. In many social science departments it is easier to find a Marxist than a Republican. In fact, it is quite common for social sciences departments to have no Republicans at all.
Many have criticized social science research for being ideologically biased and, frankly, many of these criticisms are fair. For one, social scientists have spent decades using sloppy empirical methods, or no methods at all, to make the case that conservatives uniquely possess a number of undesirable personal characteristics (e.g., prejudice and intolerance). However, as I discussed in an article for Scientific American, recent studies reveal methodological flaws of past research and show that liberals are no more tolerant or nondiscriminatory than conservatives.
Moreover, a number of the psychological concepts social scientists and activists have used to support social justice-oriented interventions and policies have not stood up well to empirical scrutiny. Take, for instance, the concept of stereotype threat. Psychologists proposed that female math performance is undermined by the existence and situational awareness of the stereotype that women are bad at math. However, the stereotype threat explanation of women’s math performance has failed multiple replication attempts. Meta-analyses have offered no support for the idea. And the original supporting research has been widely criticized as having many methodological and statistical problems. Still, many social scientists, activists, and college administrators continue to teach and champion the idea.
Unfortunately, the stereotype threat example is not an anomaly. The concept of unconscious or implicit biases as measured by the implicit association test (IAT) has also received considerable criticism. Many social scientists, activists, college administrators, and science journalists, have made empirically unsupported or exaggerated claims about the predictive power of the test while neglecting to mention or consider its many problems and limitations. More generally, the term unconscious bias is carelessly and unscientifically employed by many, including social scientists who should know better, to explain outcomes they find personally undesirable.
The microagression concept is another example. Again, many academics, activists, and college administrators are enamored with it, without scientific justification. Psychology professor Scott O. Lilienfeld summed it up perfectly with the title of his very thorough article – Microaggressions: Strong Claims, Inadequate Evidence.
The truth is, some social scientists, though certainly not all of them, and many social activists and journalists have weaponized the social sciences for ideological warfare. This has created quite a mess. One way social scientists can stand up for science is to clean up this mess and dedicate ourselves to fighting ideological bias within our fields. We have a lot to offer and much of our research is very good and has little or nothing to do with social and political alliances. However, we cannot afford to ignore the very real threat that ideological bias poses to the empirical social sciences.
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