Élèves plurilingues en classes ordinaires : outils et questions pour la didactique du français
Bibliographic record
Abstract
Ce numéro concerne la didactique du français ainsi que la didactique des langues et la sociolinguistique : il regroupe des recherches actuelles sur le plurilinguisme dans les classes dites monolingues ainsi que sur les pratiques de classe et de formation y afférant. Les enjeux sont importants en termes d’inclusion et d’efficacité de l’enseignement, surtout en France, pays dans lequel l’idéologie monolingue dominante est très fortement implantée. Les onze articles constituant ce numéro montrent une dynamique, voire une effervescence des questionnements et des expérimentations. Ils s’intéressent aux pratiques ordinaires et analysent des expérimentations ainsi que des démarches de formation pour les enseignants. Ils ont en commun d’affirmer la nécessité de valoriser la diversité linguistique et de faire du plurilinguisme un atout et un levier pour tous les élèves. Les contributions concernent différents niveaux scolaires avec une prédominance pour le premier degré. Elles traitent aussi de différents contextes sociolinguistiques francophones : la France métropolitaine, la Guyane, Mayotte, le Québec, soulignant le fait que la problématique est cruciale, en particulier dans les établissements scolaires où les inégalités sociales sont les plus marquées. This issue focuses on the teaching of the French Language, the teaching of languages and sociolinguistics. It brings together current research on multilingualism in so-called monolingual classes, classroom practices and related training. The implications for inclusion and effective teaching are huge, particularly in France, a country in which monolingual ideology is dominant and very firmly established.The eleven articles in this issue present a dynamic, perhaps even a bubbling-up of questions and experiments. They focus on normal practices, experiments, as well as training programmes for teachers. They all affirm the need to value linguistic diversity and to see multilingualism as an asset and an educational tool for all students. The texts cover different school levels, with a particular focus on primary schools. The contributions also feature different sociolinguistic contexts in the French-speaking world: metropolitan France, French Guiana, Mayotte and Quebec. They highlight the crucial importance of this issue, particularly in schools where social inequality is most pronounced.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".