Denouer le noeud/ne ou quand dire, c'est (de)faire, pour une etude pragmatique de quelques enonces negatifs dans trois pieces de Racine
Bibliographic record
Abstract
Le sujet de ce memoire est ne d'une discussion animee dans un seminaire sur Racine a l'Universite du Manitoba. A la fin d'un long apres-midi a la mi-novembre, nous discutions la fin de la piece Berenice, plus precisement, l'echange entre l'empereur Titus et la reine Berenice: "Madame, un mot."; "Non". Le debat qu'a enclenche ce dernier enonce--si simple, mais si devastateur--nous a souligne le pouvoir qu'a le langage du theatre du dix-septieme siecle, meme au milieu d'une salle de classe froide et morne quatre cent ans plus tard. La phrase "Denouer le $\\langle$noeud$\\rangle$" represente la facon dont nous abordons l'approche critique utilisee dans cette etude, qui est, comme nous le verrons plus tard, le domaine vaste et enchevetre de la pragmatique, ou se trouvent noues plusieurs concepts de la linguistique et de la philosophie du langage. "Denouer le $\\langle$ne$\\rangle$" represente la nature des structures grammaticales negatives que nous isolerons dans les pieces. Mais ce "ne" represente aussi une sortede "noeud", car nous tenterons de reveler que la negation habite les espaces de conflit dans chaque piece, c'est-a-dire, ces moments centraux ou la tension dramatique et les conflits de la tragedie s'intensifient; ainsi, la ou nous trouvons des espaces de conflit nous trouvons des noeuds de negation. (Abstract shortened by UMI.)
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.033 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".