L’humour noir comme instrument de jeu-à-la-mort dans des films récents
Bibliographic record
Abstract
Dans cet article, il est question du mécanisme ludique d’humour noir dans des films d’Anders Thomas Jensen, Quentin Tarantino, Jean-Pierre Jeunet, Ethan et Joel Coen et Olias Barco. Nous partons de la conception de l’humour donnée par Freud comme stratégie d’économie d’affects, et cherchons à l’enrichir en proposant une analyse des caractéristiques formelles du jeu entre réalisateur et public. Les films en question offrent des morts violentes traitées avec désinvolture. Même si une telle violence produit visuellement un choc, le plaisir ludique est présent qui inscrit le jeu humoristique dans une positivité d’ethos. Notre approche range la mort dans le tragique plus que dans la tristesse, à savoir dans le domaine d’une tension insoluble. Les films étudiés traitent de la mort nécessaire et cruelle sur le mode de simulacre qui permet à une multiplicité subjective de s’exprimer comme source de recomposition de sens et de joie.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".