Les dictionnaires français sont-ils favorables à l'indépendance du Québec ? Etude du marquage Québec/ Canada dans le Robert et le Larousse
Bibliographic record
Abstract
Nadine Vincent est chercheure à l’Université de Sherbrooke. Ses travaux portent principalement sur le français québécois (dans toutes ses dimensions linguistiques) et la lexicographie. Dernière publication (2014) : « Organismes d’officialisation, dictionnaires et médias : le triangle des Bermudes de la francisation », Actes du 4e Congrès Mondial de Linguistique Française (CMLF), Freie Universität, Berlin, 19-23 juillet 2104. Disponible en ligne : https://www.shs-conferences.org/articles/shsconf/pdf/2014/05/shsconf_cmlf14_01315.pdf Nadine Vincent is researcher at Sherbrooke University. Her works mainly bear on Quebec French (taken in its full linguistic scope) and Lexicography. Last publication (2014): “Organismes d’officialisation, dictionnaires et médias : le triangle des Bermudes de la francisation”, Actes du 4e Congrès Mondial de Linguistique Française (CMLF), Freie Universität, Berlin, 19-23 juillet 2104. Online access :https://www.shs-conferences.org/articles/shsconf/pdf/2014/05/shsconf_cmlf14_01315.pdf
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".