The war on standards: NIH edition

Heather Mac Donald has warned that identity politics, having engulfed the humanities and social sciences on American campuses, is now taking over the hard sciences. Here is an example:

A friend sent me this application form for the Graduate Data Science Summer Program at the National Institutes of Health. Those accepted to the summer program “will spend the summer at the NIH learning how to use their computational skills to help answer critical biomedical research questions that can have significant societal impact.”

The application states:

Students with disabilities; students who are Pell Grant-eligible; students who are or have been enrolled in Tribal Colleges and Universities, Hispanic-serving institutions, or Historically Black Colleges and Universities (HBCUs); students who identify as LGBTQ; and individuals disadvantaged by circumstances that have negatively impacted their educational opportunities, including recent natural disasters, are encouraged to apply to GDSS.

Why does NIH encourage gays, lesbians, and transgenders to apply? What interest is served by having these students, as opposed to heterosexuals, improve their computational skills. Is there any evidence that such students have been disadvantaged when it comes to obtaining such skills?

This type of “encouragement” clause is a standard way of signaling that members of the groups identified will be preferred to non-members. As my friend notes, given the comparatively small number of trans people, identifying as trans probably improves chances of selection to a greater degree than being, say, black. Yet, being black normally presents more obstacles to getting to the point of being qualified for summer work in computing at NIH than being trans.

Thus, to the extent NIH wants to prefer certain groups in order to offset barriers its members may have faced, its inclusion of transgender — and, indeed, gays and lesbians — is misguided. If the goal, instead, is to increase diversity for diversity’s sake, what’s the point? Whatever the arguments for the valuing diversity in some areas, it’s difficult to see how they might apply when it comes to the analysis of data.

NIH tells applicants to “discuss the following important elements in the application cover letter”:

Prior research experience, and current and future research interests.
Educational and career goals.
How participation in GDSSP will assist in achieving stated goals.
Commitment to building a diverse and inclusive community within the biomedical research enterprise.
Leadership experiences in school and in the community.

“Commitment to building a diverse and inclusive community within the biomedical research enterprise”? By a summer intern?

This is part attempt to brainwash and part an invitation to bullsh*t NIH. People who consider race, ethnicity, sexual preference, etc. irrelevant to data analysis — i.e. reasonable people — will either have to lie on their application or guarantee their non-selection.

Where will the obsession with diversity in math and science lead us? I agree with Mac Donald:

[L]owering standards and diverting scientists’ energy into combating phantom sexism and racism is reckless in a highly competitive, ruthless, and unforgiving global marketplace. Driven by unapologetic meritocracy, China is catching up fast to the U.S. in science and technology. Identity politics in American science is a political self-indulgence that we cannot afford.