Bibliographic record
Abstract
Dans cette contribution, je propose l’interprétation d’une réaction individuelle face à des pratiques de marquage du discours rapporté utilisées dans des médias en milieu linguistique minoritaire (l’apposition à la suite de propos présentant des particularismes linguistiques locaux d’un (sic) ainsi que divers marquages métadiscursifs type « nous dit-il avec son charmant accent acadien »). Une recherche systématique au sein d’un corpus montre que ces pratiques sont notables bien que peu courantes. Ces pratiques relèvent sans doute de techniques professionnelles bien établies. Il n’en demeure pas moins qu’elles peuvent s’avérer indélicates envers la communauté que ces médias desservent ce dont témoigne la réaction individuelle que je prends comme point de départ. Je propose que l’on peut voir dans ces pratiques une forme d’arrogance linguistique (Messaoudi 2021) constituant une micro-agression linguistique (Razafimandimbimanana et Wacalie 2020) de plus auxquelles font face les minorités linguistiques.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".