L'enseignement du vocabulaire fréquent et usuel adapté aux élèves francophones du Nouveau-Brunswick : Différentiation par l'étiquetage et approche actancielle
Bibliographic record
Abstract
Dans un contexte minoritaire comme celui des Acadiens, ou la variete de francais que parlent les eleves se trouve a etre differentes de la variete de francais enseignee en milieu scolaire, comment l'ecole peut-elle contribuer a ameliorer leurs competences langagieres, dans le but de leur permettre d'elargir leur repertoire linguistique et avoir ainsi acces aux ressources materielles et sociales qui s'ensuivent? D'apres nous, il est possible d'expliquer les differentes particularites regionales acadiennes de facon a sensibiliser les eleves aux phenomenes linguistiques de la variation. Pour ce faire, nous suggerons l'etiquetage du regionalisme et l'approche actancielle, car adaptes a l'eleve acadien, ils peuvent etre utilises pour faire ressortir les divergences entre le francais standard et le francais acadien en decrivant la structure de la phrase et les traits semantiques des actants, et ce faisant, les contraintes d'emploi lexicales et semantiques.
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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".