Insulte, disqualification, persuasion et tropes communicationnels : à qui l’insulte profite-t-elle ?
Bibliographic record
Abstract
Partant d’une acception très large de l’argumentation, nous nous intéressons ici au lien entre insulte et persuasion afin de repenser la relation persuadeur / persuadé sous l’éclairage de celle d’insulteur / insulté. Cette conception fondamentalement interactionniste place au centre de l’analyse l’objet de persuasion et la (possibilité de) satisfaction de l’acte, ainsi que toute la mise en scène, le décor et le public qui assiste à la représentation (pour reprendre la métaphore théâtrale de Goffman), sans négliger la dimension émotive de l’insulte. Insistant sur la dimension argumentative de l’insulte, nous tentons de montrer, à partir d’extraits de sites Internet d’évaluation de professionnels, que toute disqualification devant un tiers a comme visée de le persuader d’adhérer à la thèse implicite de la validité de la qualification péjorative, ce qui se manifeste, sur le plan perlocutoire, de deux manières : persuader de haïr (faire adhérer à la disqualification d’autrui) et persuader d’agir (faire poser une action conséquente avec l’adhésion à la disqualification d’autrui).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".