Articulation langue 1- langue 2 dans le répertoire langagier des élèves inscrits en programme immersif : quelles ressources lexicales pour les cours de sciences ?
Bibliographic record
Abstract
Cet article présente les résultats d’une étude visant à analyser chez des élèves inscrits en programme immersif les modalités d’articulation entre, d’une part, contenus lexicaux et contenus conceptuels et, d’autre part, L1, langue de communication familiale, et L2, langue de scolarisation. Après une mise au point sur l’état de la recherche à ce sujet, les résultats de l’analyse quantitative et qualitative sont présentés. L’expérimentation a été réalisée auprès de six classes d’élèves belges francophones de 5e année d’école primaire. L’échantillon pris en compte est constitué de 54 élèves inscrits dans un programme immersif et de 50 élèves, constituant le groupe témoin, inscrits dans un programme classique d’une école de la même commune.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 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".