For Queer Communities, Being Counted Has Downsides

Planning and undertaking a large-scale quantitative investigation of queer lives requires money, time, and resources. It makes people look busy, but this appearance of activity might not result in any positive impacts for the communities at the center of the study. Collecting more data can be a way to respond to demands for “action” while doing little to meaningfully address problems that impact queer lives.

In 2023, the Scottish Government will publish census data on the size of the country’s lesbian, gay, bisexual, and trans populations. Whatever the percentage size (whether it’s 2 percent or 10 percent), those opposed to queer rights will likely weaponize census data to further their aims. For example, if the percentage is higher than expected, opponents will call into question the reliability of the census or the borders determining who counts as “queer.” If the percentage is lower than expected, census data might support calls to strip government funding provided to LGBTQ-specific services. In these examples, insights from data into queer lives are secondary compared to outcomes the data produces and the various political goals (good and bad) it can support.

When I present research on the possible downsides of expanding data practices to include queer communities, the most frequent follow-up question is how we fight back to ensure data about queer lives primarily serves the interests of queer communities. No guaranteed solution has presented itself (if one exists), but I can suggest two paths that will help queer communities reclaim our data.

Firstly, we cannot allow anti-queer groups to curtail who “counts.” During the design of Scotland’s census, opponents pursued policies, classifications, and technologies that solidified “in” and “out” groups. The creation of two-tiered queer communities favors gays and lesbians who are monogamous and married, trans people who have legal paperwork to document their transition, and bisexuals who pick a side and stick with it. A two-tiered arrangement intersects with other identity characteristics and disproportionately elevates the status of those who are white, affluent, nonmigrant, and nondisabled. The counting of some queer people and the accompanying language of inclusion is used against all queer communities working to expand the borders and constraints of gender, sex, and sexual identities.

Secondly, fighting back requires us to think differently about data methods and to reassess our relationship to existing institutions, such as governments, national statistical offices, census bureaus, and research organizations. Many of the systems that are expanding to capture more data about queer lives are rooted in data practices that have historically inflicted harm on marginalized communities, including the use of data as “proof” of pathological problems among queer communities and the recording of male same-sex activities as “criminal.” Individuals with lived experiences of how data practices fail queer communities are better-placed to plug gaps, remedy errors, and advocate for inclusive questions, additional response options, and write-in text boxes.

Going further, the structures that facilitate data practices require critical attention. Rather than collecting more accurate or better-quality data about queer communities, we need to examine the bigger picture and reimagine data collection. The use of existing systems, technologies, and classifications provides an unreliable foundation for action and limits the potential of what queer communities can achieve with data. By trying to work within a broken system, LGBTQ+ people become participants in a game in which the existing rules mean they are destined to lose. Even with the best census questions, the gender, sex, and sexual identities of some respondents will never map to discrete, fixed response options. Some people are always going to be misrepresented by data. Ultimately, data practices that serve the interests of queer communities need to depart from standardized and static categories and reimagine quantitative data as something fluid, messy, and pluralistic that changes over time.