Locutions verbales usuelles examinées par des natifs de France et du Canada : uniformité et variation
Bibliographic record
Abstract
En nous basant sur des études psycholinguistiques consacrées aux locutions et en menant des recherches de fréquence dans Eureka.cc, nous avons choisi 34 locutions «très connues» des locuteurs français. Afin de vérifier dans quelle mesure des locuteurs natifs provenant de France et du Canada partagent les mêmes connaissances phraséologiques, nous avons proposé un test à choix multiples sur les locutions aux deux groupes, France et Canada (n=33). Les participants ont aussi noté les scores sur leurs perceptions de la connaissance, familiarité et usage des locutions et proposé des expressions qui, selon eux, étaient régionales. La connaissance de la plupart des locutions est «partagée» par les deux communautés, ce qui confirme l’uniformité des connaissances phraséologiques des natifs par rapport aux items choisis, alors qu’un petit nombre de locutions démontre une variation diatopique significative. La portée de ces résultats pour la phraséodidactique est discutée.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".