Bibliographic record
Abstract
La présence des didascalies dans l’œuvre dramatique ne relève pas d’une fantaisie de la part des auteurs. Ces derniers en usent de manière modérée ou par saturation pour accompagner les formes dialogiques à travers les circonstances qui les singularisent ou pour accéder à la psychologie du héros et des autres personnages. Comprendre la plupart des œuvres dramatiques impose au critique d’identifier les didascalies en les déclinant en catégories introductives et intégrées, en définissant un mode de décryptage qui procède par sous catégorie afin d’établir leur fonction. Il ressort de cet article que quelle que soit sa nature, toute didascalie participe du sens. Elle revêt des fonctions d’anticipation, de rythmisation et de ponctuation dissimulées dans les discours et les situations. The presence of didascalies in plays is not a matter of fantasy on the part of the authors. The latter use them in a moderated way or by saturation to go with the dialogic forms through the circumstances that singularize them or to access to the psychology of the hero and the other characters. Understanding most of the dramatic works requires the critic to identify the didascalies by organizing them into introductive and integrated categories, by defining a mode of decodage that proceed by sub-categories in order to establish their function. It emerges from this paper that whatever its nature, any didascalie participates to the meaning. It plays the functions of anticipation, rythmisation and punctuation dissimulated in the discourse and the situations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".