Les variations linguistiques à l’intérieur des locutions contenant le mot « tête »
Bibliographic record
Abstract
Introduction : les locuteurs sont susceptibles d’utiliser des locutions qui peuvent nuire à \nla transmission d’un message. Objectifs : répertorier des locutions dans lesquelles apparait le mot \n« tête » et de distinguer les variations (diatopique, diachronique, diaphasique, diastratique, \ndiagénique et chronolectale) auxquelles ces locutions sont soumises au Canada (en Ontario, au \nQuébec et dans les provinces de l’Atlantique), en France et au Burkina Faso. Méthodologie : un \nquestionnaire, accessible électroniquement et disponible en format papier, a permis de recueillir \nles données nécessaires à notre étude et d’évaluer la connaissance et l’usage des locutions chez les \nlocuteurs de notre échantillon. Résultats et conclusions : nos conclusions, qui découlent de 15 \nhypothèses, contribuent au domaine de la variation linguistique des locutions qui est peu exploré, \nselon nos recherches bibliographiques, et vérifient plusieurs théories qui ne sont pas dotées de \npreuves empiriques ou qui sont contradictoires.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".