Bibliographic record
Abstract
L’auteure analyse les liens entre le lieu, les discriminations linguistiques et les processus de minorisation à la base des hiérarchies et des inégalités sociales entre locuteurs francophones au Canada. Plus particulièrement, elle montre comment les francophones bilingues des régions périphériques qui font usage de pratiques mélangées du français et de l’anglais – dans des slogans, dans des chansons – sont souvent ciblés comme exemples à ne pas imiter dans les médias nationaux pour montrer l’appauvrissement du français au pays. Considérés comme des francophones illégitimes par les tenants de ces discours, certains locuteurs visés intériorisent ce sentiment et le tiennent pour naturel et allant de soi ; ils se cantonnent dans le silence pour éviter de se faire juger. D’autres, surtout les artistes, tentent de répondre à ces discours hégémoniques par des stratégies diverses, dont la mise en scène des traits stigmatisés. L’Acadie servira d’exemple pour illustrer ces différentes manifestations.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.020 | 0.022 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".