Translanguaging and Multilingual Academic Literacies: How Do We Translate That into French? Should We? Pour en faire quoi ? (et pourquoi s'en faire?)
Bibliographic record
Abstract
Translanguaging has become an inescapable notion in the anglophone literature on multilingual education. Questions thus arise as to whether and how it should be imported into francophone educational discourse, which has developed parallel vocabularies and orientations. The stakes are not only terminological — how to import a notion with ill-defined contours from one discursive universe to another — but pedagogical and political: what value could be added by the notion of translanguaging, either translated or borrowed as is, not only in addressing the needs of plurilingual writers who, like francophone educators, must reconceptualize English disciplinary discourses in another language, but also in creating more equitable conditions for the circulation of ideas across linguistic and national lines? To tackle these questions, this paper draws on two sources of insight: strategies for cross-lingual mediation developed in translationand terminology studies, and lessons from the import of literacy into francophone discourse in the 1990s and 2000s. Existing uses and translations of translanguaging are then reviewed, and new translation equivalents proposed to render and clarify the multiple meanings of translanguaging and operationalize the notion in French. In keeping with a translanguaging approach, the paper translanguages about translanguaging, deepening our understanding of it through more than one linguistic lens. Keywords: Translanguaging, translation and writing pedagogy, multilingual terminology, multilingual academic literacies, crosslingual reading and composing, international circulation of ideas
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".