You and I are going to use actual data to understand diversity in STEM.

You and I are now going to attempt to have a conversation about diversity and discrimination. We’re going to do it with love and respect, and it’s going to take place here, on the internet.

[NARRATOR: And they were never heard from again.]

In particular, because this is a blog about physics, I want to talk about issues of diversity and inclusivity in physics. First, the facts. It is a fact that various genders and ethnic groups are underrepresented in science, technology, engineering, and mathematics (STEM) disciplines.

In case that graph just looks like a bunch of numbers, I’ve reframed it
below. In a just and equal society, where everyone has the same opportunity
to succeed, you would expect the demographic distribution in STEM to roughly match the national demographic distribution. For example, if 17.1% of the US population identifies as Hispanic/Latino, you might expect that 17.1% of all STEM PhDs would also identify as Hispanic/Latino. A lower percentage would indicate that that group is under-represented in STEM, and a higher percentage would indicate that that group is over-represented. I’ve taken the demographic distributions of various STEM groups and normalized them to the US population to get a rough idea of who is under/over-represented.

By the way, I’ve drawn this data from the US Census [1], from the NSF study on “Women, Minorities, and Persons with Disabilities in Science and Engineering” [2], and from the Fermi National Accelerator Laboratory’s Diversity website [3]. (I think it’s great that Fermilab is tracking this information and displaying it publicly, and I wish more institutions would do the same.)

There are problems with this data, of course. For example, not everybody fits cleanly into one of the categories in the US Census. What if you’re multi-racial? What if you look Black, speak mostly Spanish, and live in the USA? (Actually, if you really want to get right down to it, “race” is a fiction, invented by people who wanted to sell slaves [4, 5, 6].) What if the gender binary isn’t useful to describe you? What box do you check? And what about LGBTQIA+ folks, who aren’t yet counted by the census and may not wish to be “out” at work? There are a lot of ways to be a person — or, per this essay, a person with a STEM job — and not all those ways are described very well by extant demographic data.

It’s interesting to debate whether race and/or gender are social constructs, but a bit outside the scope of this essay. These memes have colonized our brains, whether we want them to or not. People will judge you by the color of your skin, your perceived national origin, the language or accent they hear you speak with, the clothes you wear, etc. The question I want to ask you here is: what are the consequences for STEM institutions?

There are a lot fewer women, Black, Hispanic/Latinx, etc. folks working in scientific jobs than you might expect, given their populations in the US. Talking about this problem (yes, I think it’s a problem) immediately makes some people defensive. Or emotional. Or just scared, as if the mere mention of structural racism is enough to pitch us all onto the social media bonfire. “I just want to hire the best person for this position” is a common refrain. Or, “we care about diversity, but it’s not our fault that mostly white men responded”. Some people will tell you, despite plenty of evidence to the contrary, that the demographic distribution in STEM reflects innate differences in ability between men and women, or between various races.

This is all very lazy.

By “lazy”, I mean that you should expect more from scientists. If you recognize a problem, and the solution to that problem isn’t immediately obvious, shouldn’t this lead to more questions? Shouldn’t you be willing to consider that difficult problems might have complex solutions? If you say “racism is bad but I can’t help anything in my position as a scientist”, why is this any different from saying “this business about `luminiferous ether’ seems fishy, but it sure is a popular theory; I have no choice but to play along”.

There are better, more complete explanations for these phenomena, and they are freely available to us. Social scientists have been attacking the problem of STEM diversity for years. What does their research tell us? What are the sources of inequity, and what can we do to fix things?

This is what I want to tackle in this new series of posts. Some of my particle physics pals have formed a journal club. We meet once a month to pick through an article on diversity and inclusivity in STEM, with some help from our social scientist colleagues. I’m going to write here about what we learn.

I expect that most of what I have to say here will be about the process of learning about this issue. What does the literature say? How should we interpret what the literature says? (This is a big one for us. We’re used to enormous data sets and meticulous systematic error analyses. How should we understand studies with, like, 200 respondents?) And what can we take away from these studies and apply in our own lives?

Did we make it? Everybody still standing? Good, we’ll get into the literature next time, when I discuss our discussion of “Science faculty’s subtle gender biases favor male students” by C.A. Moss-Racusin et al., PNAS 109, no. 41, 10/9/2012, pp. 16474-16479 [7].

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One thought on “You and I are going to use actual data to understand diversity in STEM.

  1. Pingback: John vs Jennifer: In which the journal club discusses gender bias and has a collective epiphany | Midwest Science

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