Bibliographic record
Abstract
Résumé : Le film Crash (Collision) de Paul Haggis (2004) emploie la fluidité des rôles de genre pour illustrer des cas d’iniquité raciale et les réactions qu’ils suscitent. L’auteure démontre de quelles façons le film joue avec l’idée que se fait Frantz Fanon du regard raciste comme forme de castration métaphorique — le remplacement systématique de la puissance sexuelle masculine par une objectification et une soumission forcées. Dans l’esprit des travaux de Fanon, Daniel Kim déclare que le regard homosocial du mâle blanc « force les hommes de couleur à féminiser leur position », affirmant la supériorité raciale à travers le langage de la domination sexuelle. Dans Crash, Christine (Thandie Newton) et Cameron (Terrence Howard) Thayer sont tous les deux privés de leur identité sexuelle à titre de gens de couleur. Bien que cette castration métaphorique ne soit pas nécessairement permanente, elle met en relief le lien inéluctable entre race et genre et les pairages qui tendent à dicter la domination et la soumission.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".