Bibliographic record
Abstract
Résumé L’article s’intéresse à la gestion de la confrontation d’un même politicien québécois, Jean Charest, sur un même thème dans une émission d’affaires publiques et dans un talk-show . À partir d’un cadre d’analyse du discours d’opposition (Vincent et al. 2008), l’étude décrit les choix communicationnels faits par le politicien en interaction pour concilier la protection des faces avec les contraintes associées au type d’activité. Si l’expérience médiatique des émissions d’affaires publiques contribue à établir un horizon d’attentes relativement stable quant aux comportements à produire, les talk-shows placent les politiciens dans une situation plus complexe : comment agresser l’autre, l’amener sur son propre terrain, le dominer tout en répondant aux attentes de l’activité (en termes de complicité, de proximité et d’interaction ludique) ? L’étude de ce cas montre que l’amplification ou l’atténuation de la tension entre les participants est au service de stratégies fines de présentation de soi distinctes selon l’activité.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".