Bibliographic record
Abstract
This commentary responds to Celia Kitzinger and Sue Wilkinson's argument for the use of human rights discourse rather than a discourse of mental health when arguing for the legalization of same‐sex marriage. Without disagreeing with their basic argument, I “problematize” it, showing that legal and human rights discourses also have a history of reinforcing power dynamics and operating to the disadvantage of marginalized groups such as lesbians and gay men. First, equality rights discourse can force lesbians and gay men into a conservative mode of argument, for instance, having to show how similar they are to traditionalist opposite‐sex couples, rather than emphasizing potentially significant differences. Second, the increasing use of rights discourse has arguably narrowed the scope of the lesbian/gay social movement and rendered its political strategies more conservative, rather than aiming for the elimination of heterosexism and patriarchy. Third, the focus on marriage as a human right tends to render invisible, and to reinscribe, the extent to which marriage as a socio‐legal institution has operated in oppressive ways. Modern marriage is not innocent of oppression, tied as it is to the increasing privatization of social and economic responsibilities. While human rights discourse offers an important avenue for lesbians and gay men, the perils of its use should not be overlooked.
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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 | 0.025 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.054 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.062 | 0.050 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".