The social construction of gay oppression as a determinant of gay men's health: ‘homophobia is killing us’
Bibliographic record
Abstract
Where once the health sciences concentrated much of their efforts on curing homosexuality, contemporary health science is concerned largely with the mental and physical effects of oppression, which are now taken as the legitimate focus of health research and intervention. However, gay men's health has imported into its practice a fairly individualistic ideology, one that undermines efforts to address the social determinants of health and to achieve social justice goals. In this article, I explore the social construction of gay men's health in the health literature. My analysis focuses specifically on constructions of gay oppression as a determinant of health. Through an analysis of the gay men's health literature, I identify three recurring themes: (i) gay oppression is conceived as a psychological phenomenon, (ii) gay men's damaged psychologies are a determinant of gay men's health, and (iii) individual therapies are proposed as the solution to gay oppression. Like mainstream health research and practice, gay men's health focuses attention on the mental and physical functioning of the individual, ignores the social and structural determinants of health, and directs health intervention squarely on the individual. I propose an alternative (anti-oppression) research agenda; one that shifts our conceptualisation of the ‘the problem’ from the oppressed to the oppressor.
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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 | 0.004 | 0.003 |
| 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 | 0.033 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".