Not your unicorn: trans dating app users’ negotiations of personal safety and sexual health
Bibliographic record
Abstract
This article reflects on 14 Australian trans dating app users’ accounts of feeling safer (and less safe) when using apps, as well as their experiences of sexual healthcare. We explore both app use and healthcare in the context of the interdisciplinary field of ‘digital intimacies’, considering the ways that digital technologies and cultures of technological use both shape and are shaped by broader professional and cultural norms relating to sexuality and gender. Drawing on Preciado’s [(2013). Testo junkie: Sex, drugs and biopolitics in the pharmacopornographic era. The Feminist Press] framework of ‘pharmacopornographisation’, the analysis aims to contextualise participants’ experiences of being ‘seen’ and ‘known’ by health professionals and other app users. Our findings indicate that both dating apps and sexual health services rely on reductive systems of sorting and categorisation that reinforce binary understandings of genders and sexualities in order to facilitate data management and information sharing practices. Yet these same sorting and filtering technologies can also help trans app users avoid harassment, form intimate connections and seek appropriate healthcare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".