L’anticipation d’objections : prolepse, concession et réfutation dans la langue spontanée
Bibliographic record
Abstract
Cette étude porte sur la prolepse, la concession et la réfutation lorsque ces procédés sont utilisés pour minimiser ou rejeter, par anticipation, la portée argumentative d’un contre-argument potentiel. Nous cherchons à cerner les caractéristiques, fondamentalement diaphoniques, d’un domaine productif de l’argumentation, celui de la prévention explicite d’une objection éventuelle de l’interlocuteur. Dans un premier temps, nous situons la prolepse, la concession et la réfutation l’une par rapport à l’autre. Nous concluons que la prolepse est une stratégie argumentative à quatre constituants qui doit être analysée comme l’expression des tensions subies par le locuteur pour construire un discours cohérent avec son univers de croyance et une relation acceptable avec son interlocuteur.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".