Hated Identities: Queers and Canadian
Bibliographic record
Abstract
En s’inspirant de la théorie gaie et du post-structuralisme, les auteurs analysent deux actes de violence a ̀ l’endroit de personnes gaies, soit les meurtres de M. Alain Brousseau et de M. Aaron Webster. Ils soutiennent que, dans les deux cas, le mode d’application des lois contre les crimes de haine révèle la nature problématique des efforts déployés pour figer juridiquement l’identite ́ sexuelle. Selon la loi, le fait d’être gai serait inne ́ et évident. Or l’analyse démontre que l’identite ́ sexuelle est dynamique et tributaire de divers facteurs et qu’elle n’est pas forcément auto-determinée grâce à l’application des lois contre le crime haineux. Les collectivités politisées, les acteurs judiciaires, les agresseurs et les médias participent tous a ̀ la démarche visant a ̀ « nommer » et a ̀ figer l’identite ́ gaie. Drawing on queer theory and post-structuralism, this article explores two ‘‘gay bashings,’ ’ the murders of Alain Brousseau and Aaron Webster. In both cases, we argue that the application of anti-hate crime legislation reveals the troubling nature of attempts to legally fix sexual identities. The law imagines gayness to be innate and obvious. These cases show that sexual identity is fluid and contingent. Our study also shows that, through the application of hate-crime law, sexual identification is not necessarily self-determined. Politicized communities, legal actors, assailants, and media all participate in naming someone’s ‘‘gayness.’’
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.055 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 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".