Conversations autour du plurilinguisme. Théorisation du pluriel et pouvoir des langues
Bibliographic record
Abstract
Tandis qu’on note depuis quelques années une floraison de concepts cherchant à mieux théoriser la complexité et le pluriel en didactique des langues, ces nouveaux termes ne servent-ils qu’à dépoussiérer ou remettre à la mode des notions antérieures ? Entrent-ils en conflit ou en résonance ? Et comment ?Cette contribution vise à poser quelques repères au sein d’un parcours situé, subjectif, à travers quelques jalons qui marquent la théorisation de la compétence plurilingue et pluri-/interculturelle (CPP) en Europe, notamment francophone. On discutera comment ces conceptualisations du plurilinguisme entrent en écho avec un autre concept du champ aujourd’hui largement circulant, celui de translanguaging. La contribution invite à resituer quelques espaces-temps du dialogue académique et invoque le pouvoir des langues dans la complexification conceptuelle. Mots-clés : plurilinguisme, compétence plurilingue et pluriculturelle, translanguaging, didactique du plurilinguisme
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".