Les noms propres dans le vocabulaire politique québécois : pour une approche lexiculturelle
Bibliographic record
Abstract
C’est récemment, depuis la fin du XX e siècle, que les linguistes s’intéressent aux noms propres et que les lexicographes commencent à intégrer cetteréflexion au sein même de la partie langue de leurs dictionnaires. Ainsi, lesnoms propres dans le vocabulaire politique, pour se limiter à un seul domaine,constituent-ils des éléments indispensables de la langue-culture. Bien des incohérences sont encore constatées dans le traitement des noms propres relevantdu vocabulaire politique au sein des dictionnaires usuels d’où l’intérêt de lesrepérer, tout en mettant en place des stratégies. Une conclusion s’impose : lesnoms propres du vocabulaire politique sont aussi importants que les noms communs pour comprendre une langue-culture, ici celle du Québec.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".