Photo of Jon Freeman
Jon Freeman. Image credit: New York University

Jon Freeman (he/him) is Associate Professor of Psychology and Neural Science at New York University and director of the Social Cognitive and Neural Sciences Lab. His research focuses on the cognitive and neurobiological mechanisms underlying split-second social judgments and less conscious forms of bias. 500QS' Simon Morrow talked with Freeman about his recent review of LGBTQ disparities in STEM.

(Links: Twitter/Website/Lab)

Ed note: This conversation has been condensed and lightly edited for clarity.


How did this project begin?
I got involved in this from a Twitter thread in 2018, where a colleague of mine showed me a graduate student's thread. It was a graduate student in neuroscience, and he'd written on Twitter about a discussion with professors and other members of his department, and they'd essentially told him that there's no point in being out in science because acceptance is guaranteed and implicit acceptance exists and it's irrelevant—essentially in that there's no point in being out, and that reason why faculty, for example, aren't out is because it's irrelevant, and people are accepted no matter what. So from my own personal experiences in STEM, and from experiences in graduate school and faculty positions, etc.—and [as someone] about to receive tenure with some protection—this really enraged me. And I then wrote a piece in Nature that expanded on that, looked at the data on this issue. And I think coming from social psychology, and knowing a variety of prominent researchers who study the women in STEM literature, or racial disparities in STEM literature, or how these things intersect, but not talking about LGBTQ disparities, when I looked at the very few studies that exist, it alarmed me in terms of this blind spot in why this hasn't received more attention.

So I wrote that Nature piece, and what I realized from my own review of the data and of the literature about how this works, is that the federal government is not tracking these disparities and connecting the dots between how important federally collected data are for gender and racial disparities, and all the kinds of federal resources and policies that are involved in gender and racial disparities, and how these things intersect, which have been the case since at least 1980.

I think you need to have very strong data to inform interventions, and to attract more researchers to this issue, because this is an issue certainly that affects LGBTQ individuals; it also affects non LGBTQ individuals as well. If barriers are hindering LGBTQ people, those are barriers that are placed on the U.S. STEM workforce generally, and what the 1980 Science and Engineering Equal Opportunities Act recognized—and what Congress explained in signing it—was that this is, on the one hand, an equal opportunity issue, but it's not just that right. It is imperative that all individuals who wish to contribute to science not be hindered in their opportunity to do so because it's in the national interest to maximize the talents of the U.S. STEM workforce because there are urgent, complex scientific challenges that our world faces, and if we're going to solve them, we need everyone on board, and no one should be hindered from that.

Of course, both are important, but from my perspective, the U.S. STEM education and workforce literature's federal resources need to pay attention to LGBTQ disparities in STEM not just to promote the welfare of LGBTQ individuals, because not all policymakers are necessarily on board with that, but what we all can agree on is that if there are impediments to the U.S. STEM workforce due to LGBTQ barriers, for example, then we need to solve them to be able to solve our complex and urgent scientific challenges.

[Ed note: After the publication of the Nature piece, Freeman began the process of formally requesting that the National Science Foundation add sexual orientation and gender identity questions to existing surveys collecting data on America’s STEM education and workforce. But despite a promising initial meeting to discuss the issue, NSF has yet to even pilot the questions.]

Can you tell us where the current effort stands now?
All we're asking for now is simply that these demographic measures, which exist in a variety of other federal surveys, be added to NSF surveys. Other federal surveys have included these questions for sexual orientation and gender identity; OMB [the White House Office of Management and Budget] has approved those questions. There are a variety of Federal Statistical experts that have studied these questions and how they behave on these surveys, making sure that people are comfortable with them, that they understand them across different languages, that demographically everyone knows how to do this, that there aren't response biases. So these things are extensively studied by both Federal Statistical experts and the independent research community, and all we're asking is that those questions—which have been studied and included on other federal surveys—be added to NSF surveys.

Why are these surveys are important? What impact would they have?
They're the only ways we know about critical information. Too give you a couple of examples, these surveys and reports are the only way we know that over the past 20 years, female and black undergraduates have actually become less represented in the fields of math and statistics. That although in most other STEM fields Black people are increasingly represented, they're still experiencing pay inequality. Or that computer science suffers from the worst under-representation of women across STEM fields. So if we don't have these surveys, we're left really blind to so many critical issues in terms of resolving these issues. And all that data comes from these three surveys.

What do you expect to happen once these questions are included?
There are hundreds of researchers who study gender and racial disparities in STEM, and as soon as these data are available, they're going to immediately be analyzing these nationally representative data sets and looking for issues—looking for pipeline issues, looking across career stages, looking across regions, looking across where exactly are the disparities, if they're occurring, happening.

And then this can help to paint a picture that would have a variety of effects. One, it would lead to an explosion of published studies on the issue and analyses figuring out what's going on with LGBTQ individuals in STEM. It would probably then create this snowballing effect, where it creates yet more published studies, attracts more researcher attention. NSF and NIH will probably start funding more research grants devoted to this issue, which gets more researchers to study it because of course, STEM education researchers are driven by what's funded, and right now NSF and NIH, due to the way that STEM education and workforce research is funded, doesn't necessarily include LGBTQ identity.

What can others do to support this effort?
Acknowledging and communicating the value of demographic data collection of sexual orientation and gender identity is probably the most helpful thing that people could do. The more support and the more understanding that the LGBTQ community in STEM has for the need for demographic data, the better.

What are some of the most common misconceptions you encounter?
What I've encountered is a kind of knee-jerk reaction to inclusion of these measures, as if it's going to open LGBTQ people up to potential discrimination because there aren't federal protections. There's a lot of very understandable, but misconstrued notions about what data collection would do.

The privacy and confidentiality of individually identifiable data, including these questions, is always strongly protected by federal law. They're not going to open people up to potential discrimination. They're voluntary, just as any other demographic question.

Overall, how would you describe the importance of visibility of LGBTQ people in STEM?
Same gender and same-race peers have long been noted to increase retention, increase women's and racial minorities' sense of belonging and identification with STEM. But with LGBTQ individuals, it can be relatively hard to find other role models and peers, not only because it looks like there's significant under-representation, but because LGBTQ identity is often not visibly apparent. As I talk about in the review piece, there are also unique norms in STEM about impersonality, about being objective, that causes people—to a greater extent than in other industries—to compartmentalize their personal and professional life, which discourages identity disclosures and being open about LGBTQ identity.

I don't think that we're going to solve all these issues until we have accountability; until we have demographic data-collection on our STEM education and workforce systems and can create policies that ensure that those things don't happen. And I think that complements the incredibly necessary role that visibility campaigns, connecting, role models, and cultivating STEM identity have.

But I have concerns that no matter how much sense of belonging and identity an LGBTQ individual may have, there are still forces that are going to be working against them that might even be considered benign and well-intentioned. Faculty search committees and graduate application admissions committees may be thinking that this individual is not really a good fit for the local or the institutional or the departmental culture because of their own implicit stereotypes about [for example] LGBTQ individuals as being urbanites, and that their department's in a non-urban location, and they're just thinking for the welfare of the person. That's really a very inappropriate decision. And that can be seen as very well intentioned, even though it's not fair. And so there are so many ways that I think this is working against LGBTQ individuals in STEM, and I think the more that our community recognizes that and can work in their own local ways to do that, the better.

Final thoughts?
Disparities can't be reduced if we refuse to measure them. If we're not measuring these, federal policies can't do much and researchers, universities, etc. are left blind.

And don't be scared of demographic data collection. There are always "I don't know" responses or "I don't wish to respond" responses, and they're protected by federal law in terms of confidentiality, just like any other demographic question.