Un modèle pour l’alternance de langue sous la contrainte d’équivalence
Bibliographic record
Abstract
Comment peut-on combiner les grammaires de deux langues afin de construire un modèle du comportement des bilingues? Plus spécifiquement, est-ce possible de trouver un modèle de l’alternance de langue qui est soumise à la contrainte d’équivalence sur l’ordre des mots? Nous répondons à cette question au niveau formel, en nous servant de grammaires indépendantes du contexte qui rendent compte du comportement unilingue et du comportement bilingue. Notre solution satisfait à plusieurs conditions qui portent sur l’identification de la langue à assigner aux constituants de la phrase. Nous montrons comment adapter la théorie des grammaires probabilistes à la prédiction de la fréquence de points d’alternance possibles dans une situation donnée de bilinguisme et nous l’appliquons à quelques paires de langues qui diffèrent entre elles par rapport à l’ordre des mots.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".