L' emprise de l’homophobie internalisée : soumission et autodestruction identitaire comme mécanismes de contrôle social dans Baisers cachés
Bibliographic record
Abstract
Cet article part du postulat que les affects sont des expériences corporelles que la société disciplinaire et régulatrice cherche constamment à contrôler. Par une analyse du personnage de Louis dans le téléfilm français Baisers cachés (2016), j’explore la manière dont l’affect se métamorphose en un système d’affects complexe et sophistiqué – pensons notamment au phénomène de l’emprise – qui donne sens à ce que le sujet queer croit être son intériorité la plus profonde. En m’appuyant principalement sur les théories de Brian Massumi et de Michel Foucault, j’étudie la soumission et l’autodestruction identitaire en tant que mécanismes de contrôle social au service du système hétéronormatif. Concrètement, je montre qu’en intériorisant et en actualisant l’homophobie que lui infligent les institutions de la famille et de l’école, Louis devient lui-même un agent de ce système dont le but premier est l’autodestruction de sa propre subjectivité queer.
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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 | 0.001 | 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 | 0.007 | 0.019 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".