Grind Your Way Out: The Construction of Hegemonic Homomasculinities Among Young Hong Kong Grindr Users
Bibliographic record
Abstract
Existing literature suggests that Grindr and other similar apps manifest a pattern of social exclusion along the lines of body and gender. However, these studies are primarily conducted outside the context of Asia. Inspired by Connell's hegemonic masculinity and Duggan's homonormativity, I adopted the term "hegemonic homomasculinity" to explore the cultural hegemony at the intersection of masculinity and homosexual practices. Employing qualitative, semi-structured interviews, this study draws on 20 young Hong Kong Grindr users with diverse social backgrounds to examine the cultural hegemony within this gay online space. Through desiring muscular/athletic bodies, straight-acting men, and "healthy" sexual practices, users marginalize undesirable gay men and reproduce the cultural hegemony. Although the subordination of undesirable bodies and homomasculinities is evident in Grindr, the findings also suggest potential room for negotiation and non-conformity. Due to the emphasis on cultural politics in the establishment of Hong Kong's gay identity, neoliberalism facilitates a homomasculine ideal that is not only based on success in the realm of career, education, and family but also on a responsible body that is healthy. LGBTQ+ activists and organizations may allocate more resources to address everyday discrimination within the online gay community.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".