Queer Cultures in Digital Asia| Strategic, Conflicted, and Interpellated: Hong Kong and Chinese Queer Women’s Use of Identity Labels on Lesbian Dating Apps
Bibliographic record
Abstract
Dating apps have become an indispensable part of lesbian lives in China and Hong Kong. These platforms give queer women the choice to use identity labels to describe their gender presentations and dating preferences. For example, T andTB signify masculine presentation; P and TBG signify femininity and attraction to T and TB; H describes in-between-ness; pure refers to feminine women who are exclusively attracted to other feminine women; and no label indicates a rejection of all labels. Drawing from seven in-depth interviews and participant observations, this article illustrates how these women creatively interact with the apps’ affordance to strategically self-present. It demonstrates that these women feel ambivalent about using labels, which they see as both effective and restrictive, and argues that their app experiences can directly shape their own self-identity. Finally, this case study provides important insights into the challenges identity categories pose for dating app users of nonnormative sexuality and gender, which might be relevant to other cultural contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".