Bibliographic record
Abstract
Ce dossier rassemble des textes qui se proposent d’analyser les fonctionnements de la variation linguistique du français en discours politiques et médiatiques dans son rapport aux normes et aux évaluations qu’elles autorisent. Il accueille des études concernant aussi bien la variation phonétique (l’« accent ») que les variations de nature grammaticale (utilisation de certains formes verbales) et lexicale (régionalismes, emprunts). La mise en évidence de telle ou telle variation, en regard de la norme (usuelle, légitime…) et surtout de ses effets (distinction, discrimination/stigmatisation…) permet de prendre toute la mesure du poids des représentations sociolinguistiques des Français et des Québécois concernant, ici, l’exercice de la langue française sur ce marché linguistique particulier qu’est la communication politico-médiatique. This issue deals with the connections between linguistic variation in political and media discourses and the judgement this variation from the standard french may imply. It is composed by studies in phonetic variation (the « accent ») and grammatical (use of specific verbal forms) and lexical changes (regionalisms, moan words). The specific variation from standard french and the different kinds of social consequences (distinction, discrimination / stigma...) of this variation shows the importance of sociolinguistic representations that French people and Quebecers have on their tongue on this specific linguistic market that is the political and media communication.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".