Tinder’s lesbian digital imaginary: Investigating (im)permeable boundaries of sexual identity on a popular dating app
Bibliographic record
Abstract
Dating apps have received rapid uptake, with Tinder as one of the most popular apps in the heterosexual market. However, little research has investigated the experiences of women seeking women (WSW) on this app. This article combines two interview studies of WSW in Australia, Canada, and the United Kingdom to investigate their self-presentations of sexual identity on Tinder. By configuring settings to “seeking women,” participants perceived they were entering a space conducive to finding WSW. However, men, couples, and heterosexual women permeated this space, heightening the need for participants to signal non-heterosexual identity. Their signals fused references to lesbian and queer culture with Tinder’s infrastructure to evoke a digital imaginary, as a routinized set of practices imagined to resonate with a shared community. Although signals within this digital imaginary were sometimes playful and ambiguous, their default toward a recognizable lesbian identity often rendered other sexual or gender identities invisible.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".