« C’est du thick1 » : penser la violence et le langage dans la pièce Rouge gueule d’Étienne Lepage2
Bibliographic record
Abstract
Le présent article se penche sur la violence dans le langage comme modalité de négociation avec le réel dans la pièceRouge gueuled’Étienne Lepage (2009). Inscrivant notre démarche à la croisée des études littéraires et théâtrales, à la suite des travaux de Marion Chénetier-Alev sur l’oralité au théâtre (2010), nous exposons à la fois la violence faite au dispositif théâtral et aux lecteurs-spectateurs dans l’espace du théâtre rendu possible par la cruauté du langage. Notre réflexion se pose également dans une visée plus large, interrogeant l’inscription du théâtrein-yer-facebritannique (dont Sarah Kane est emblématique) et de ses répercussions dans le théâtre québécois contemporain, en soulignant la connaissance de la dramaturgie québécoise dont fait preuve la pièce. En ce sens, le langage inventé par Lepage offre le contrepoint à un certain cynisme contemporain et impose un langage riche et conscient de son histoire.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.021 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".