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Short abstract:

Gender identity measurement, beyond a binary sex question, is increasingly included in survey research to better understand trans and nonbinary populations. This paper investigates negotiations between researchers and gender diverse communities in producing usable data from the messiness of gender.

Long abstract:

In recent years, gender diversity has become increasingly datafied; research into trans population sizes has expanded beyond a clinical “prevalence” of rare diagnosis, restricted by gatekeeping, to a self-identified demographic category, with terms like “transgender,” “nonbinary,” and “assigned sex at birth” becoming more normalized through their appearance on forms and surveys. This paper analyzes trans and nonbinary population measurement in the US, where some federal surveys have added gender identity questions, but there is no nationally representative census data on gender identity or trans populations. Political polarization and a lack of standardized measures lead to inconsistencies as questions are skipped in some states, pulled from testing during political administration changes, or updated to reflect terminology shifts. In this paper, perspectives of the “counters” and the “counted” are explored through interviews with researchers developing best practices for measuring gender identity, and interviews with trans and nonbinary people who weigh benefits and risks of disclosing their identities amidst political polarization about trans legal recognition and protections. Measurements of trans and nonbinary populations enact both researchers’ and respondents’ understandings of sex and gender: researchers weigh different evidence bases to decide which measures and categories of sex and gender are most appropriate for their work, while respondents navigate visibility and safety alongside epistemic violences inherent to categorization and quantification of an identity that can be fluid, multiple, and personal. Analysis centers tensions between queer data as modern representation/inclusion (Guyan 2022) and a “queer approach to data” in questioning methods, ethics, and categories (Keilty 2023).