Translanguaging et intercompréhension - deux approches à la diversité linguistique ?
Bibliographic record
Abstract
C’est la compétence plurilingue qui constitue l’un des objectifs centraux de la politique linguistique de l’Europe. Le translanguaging (TL) comme l’intercompréhension (IC) sont des conceptions proposées comme appropriées sur le chemin vers une diversité linguistique vécue. Les deux concepts se situent dans le champ de recherche en plurilinguisme comme approches positives à la diversité linguistique. Incontestablement, ils représentent un enrichissement dans le débat scientifique autour du plurilinguisme. Néanmoins, les frontières et interfaces entre TL et IC restent floues de sorte que la question d’une définition pertinente se pose généralement. À cet égard, la présente contribution se penchera sur la clarification des notions en question. L’objectif de cet article est donc de mettre en évidence quelques convergences et divergences épistémologiques entre les deux approches. Afin de compléter la perspective théorique, seront discutés quelques exemples d’une enquête menée à ce propos parmi des acteurs universitaires dans le domaine du plurilinguisme, les participants du Colloque du CCERBAL 2018. Mots-clés : translanguaging, intercompréhension, plurilinguisme, approches plurielles, éducation plurilingue et interculturelle
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".