Looping Effects and the Science of the Trans Experience
Here’s a familiar picture of science: the world serves up a plateful of “raw” data for the attentive scientist to process and theorize. In this picture, the further removed the scientist is personally and emotionally from that data, the more objective and accurate their theories will be. But this isn't right, especially when it comes to the human animal. The trouble is that people don't produce the right kind of "raw" data. Take, for instance, an example near and dear to my own heart: the average trans person does not make for a good research subject. We have the bad habit of reading what's written about us and acting on that information when we talk to doctors. Even the rawest data is already corrupted before you can even run a decent regression analysis on it. Solving this problem requires connection with rather than detachment from one's subject.
Some writers see things differently. For the July/August cover story of the The Atlantic, Jesse Singal wrote about desisters: people, especially young people, who initially identify as trans and later desist. Singal himself is not trans, and in private messages that were leaked after his cover story was published, it became clear that that familiar picture of science underlies his argument about trans people. He defends himself saying,
"On other issues, of course, I would trust trans people more than anyone else—who better to talk about the humiliation of living in a state with a 'bathroom' bill, or the difficulty of getting hormones, or other stuff that only trans people have to deal with? But overall, no, I don’t think trans people are more qualified to write about the tricky science stuff going on here than I am. I’d just be lying if I said otherwise."
According to Singal, trans people may have authority over their experiences, but when it comes to theorizing, the trans experience is raw data for others to do the “tricky science” with. But reality does not cooperate with this straightforward picture, which I’ve learned from my own life.
I knew I wanted to be a woman years before I called myself trans. At the time, I was convinced that being trans required certainty that one was already a woman "trapped in the wrong body,” a theory that had percolated into my skull circa 2008. Consequently, I was quite convinced that I wasn't really trans, that my desire wasn't legitimate or authentic but merely a strange sort of fantasy I couldn't let go of. It wasn't until years later (accepting friends later, lots of feminist and gender theory later) that I first walked into a doctor's office to seek an end to my dysphoria – an end to loathing myself all the days of my life.
But in order to receive medical treatment for dysphoria, I had to spend several hours talking to various psychologists. With each new therapist, I've gone through a sort of intake interview with a scripted set of questions, such as "Do you see the world in a more masculine or a more feminine way?" The first time I was asked this I thought that it was a strange sort of question and that I see the world in a number of different ways. The (very understanding) psychologist noted that my response seemed pretty interesting and he'd love to talk about it — but he also needed to put down "masculine" or "feminine" on his checklist. After that, I knew the score: as he asked me 20 or so more questions, each of which basically amounted to "but are you really a woman or a man?", I gave him the answers he needed. Then I walked out with an appointment with an endocrinologist.
These stories are instances of what philosopher Ian Hacking called "the looping effect of human kinds." The theory of trans – call it Theory T – itself influences the raw data scientists rely on. Trans people sometimes conform themselves to Theory T's dictates; at other times, we run from it as hard as we can. We might run because of external pressures (poverty and the need to pass, social pressure from peers and parents) or for more internal reasons (as in my case). This is not obvious to scientists precisely because we are at some pains to hide it from them. Theory T is already hard at work shaping the world before scientists ever get round to collecting their data — our theories loop back around and create the very data that confirm them.
Looping effects are particularly relevant to the debate over desisters. Delta, one such case that Singal explores in his covery story, is a teenager dealing with anxiety, depression, and questions about gender, which culminated in a desire to go on puberty blockers. Delta's therapist encouraged dealing with their depression first, then proceeding with gender issues. Once Delta started on a run of antidepressants, their desire to transition faded away.
What Singal neglects to mention is that Delta's mother is a member of an anti-trans hate group. This puts Delta's carefully worded "…once I actually started working on things, I got better and I didn’t want anything to do with gender labels — I was fine with just being me and not being a specific thing" in a much different light.
A person more familiar with the trans experience can detect Theory T's influence here. If you know how to hear what isn't said, you could notice Delta does not say: "I am a happy cis girl." A trans person familiar with the hate that family can provide might wonder if this is a strategic choice made by a person still largely under the control of their parents. Or maybe it isn't. It's hard to tell — and that's the point. The raw data simply doesn't tell us if Delta is an example of a girl who was just confused for a while or a young man desisting due to social pressure.
What this means is that if you aren't willing to listen to trans people, if you think our experiences are irrelevant to your tricky science, you won't merely miss out on our experiences, you'll do bad science. You'll ignore the complications hiding behind the data and go on to create skewed theories. Those theories will be internalized by parents, doctors, and trans people themselves. And these theories will continue to corrupt "raw" data in ways that you're blind to. This is the trickiness of trans science.
Diversity and inclusion in science are not merely political goals. They are necessary for good science. Further examples are not hard to find. In 1995, researchers "discovered" a "word gap" between poor black children and middle-class white ones by ignoring differences in child-rearing practices, a finding which fed back into years of education policymaking and pop-culture understandings of black parenthood. Researchers who study sex workers consistently recruit their subjects from prisons, since they are easier for researchers to access. These studies are then generalized across all sex workers. But by including people of color and sex workers in the research process, analysts and scientists can formulate different questions and look for answers in places beyond the majority's experience.
The trouble with the widely accepted picture of objective science isn't merely that it's wrong but that it’s often used as a club against marginalized people. Singal is happy to use trans people's experience as evidence against our objectivity: our emotional attachment to the subject supposedly makes us irrational, subject to groupthink and failures of proper scientific objectivity. In reality, those experiences aren't an obstacle but an asset. When marginalized people are excluded from the scientific process, this is not merely an injustice done to us as we miss out on employment opportunities. Everyone misses out on the truth about us. And both science and society are the worse for it.