L’influence du translanguaging dans la construction identitaire des plurilingues
Bibliographic record
Abstract
Étant donné que le translanguaging et le plurilinguisme sont deux dimensions peu abordées en didactique des langues, il est pertinent de se pencher sur cette question de recherche: De quelle manière le translanguaging influence-t-il la construction identitaire des plurilingues? Dans le cadre de cette revue de littérature, nous analysons trois études empiriques (Helm et Dabre, 2020; Ollerhead, 2019; Wei, 2011) qui sous-tendent que le translanguaging renforcerait l'identité des apprenants, ce qui les encouragerait à exprimer leurs pratiques culturelles et sociales afin d'élargir leur identité. Ces avantages nous laissent penser que les pratiques de translanguaging devraient être instaurées en classe de langues. L'enseignant qui souhaite intégrer ces pratiques en classe doit toutefois fournir des ressources suffisantes aux apprenants et appliquer certains principes afin de respecter les langues minoritaires régionales.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".