Le repentir en politique sur la scène canadienne (2005-2014) : les risques de l’équivoque
Bibliographic record
Abstract
Cet article propose une analyse de contenu des discours de repentir produits par des politiciens canadiens entre 2005 et 2014. À partir d’une approche interactionniste, cette étude examine les formes que prennent ces discours, des formes qui se situent sur un continuum allant des formes exemplaires à des formes simulées de repentir. Si l’analyse montre la prédominance des marques d’équivoque dans ce discours, elle révèle également que 1) le pseudo-repentir s’incarne dans des formes variées, reflétant la tension entre le désir du politicien de s’amender et celui de protéger son image ; 2) le degré de simulation du repentir s’explique en partie par le contexte de la controverse où les réactions des publics de l’offenseur, la nature de l’offense, la réputation de l’offenseur et le rapport de pouvoir entre l’offenseur et l’offensé jouent un rôle dans la sélection d’une forme de repentir.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| 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".