Grinding against HIV discourse: a critical exploration of social sexual practices in gay cruising apps
Bibliographic record
Abstract
Social networking applications (SNAs), such as Grindr, are shaping the identities and sexual practices of gay, bisexual and other men who have sex with men (GBM). This qualitative study aimed to gain a deeper understanding of the role of such technologies in social sexual practices, particularly in relation to risk management and prevention of HIV and other sexually transmitted blood-borne infections (STBBIs). Poststructuralism and queer theory were used to critically examine the relationship between GBM and SNAs in a sample of people who use Grindr. Sixteen people, identifying as men who used Grindr, were interviewed. Discourse analysis was employed to critically examine the relationship between GBM and SNAs, and three threads of discourse emerged: Language and images, Filtering, and Trust. These threads of discourse provide insight into how the sexual beliefs, values, and practices of GBM are shaped on SNAs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.031 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".