Réalisation de portraits de langues dans le cours d’anglais langue additionnelle
Bibliographic record
Abstract
Les portraits de langues (PDL) sont de véritables portes d’entrée vers la découverte du vécu et de l’identité linguistique d’un individu. À partir d’une silhoue=e pré-dessinée ou non, ils consistent à représenter visuellement les diverses langues dans des parties de ce corps imaginé. Ce sont dès lors des outils précieux afin de (se) rendre compte de la richesse de la diversité des répertoires langagiers. Dans l’enseignement de l’anglais langue additionnelle (désormais EALx), comme celui d’autres langues additionnelles, les PDL font leur preuve dans la reconnaissance du plurilinguisme chez les personnes apprenantes. Pourtant, les PDL semblent avoir un potentiel encore plus important. Cet article propose d’engager une conversation sur le potentiel décolonisateur des PDL en tant qu’activité pédagogique dans l’EALx. En présentant et en prenant appui sur un projet-pilote de PDL visant des personnes apprenantes préadolescentes sur l’île française de la Guadeloupe, les autrices discutent de la portée critique de l’utilisation des PDL dans l’EALx afin de contribuer à déconstruire la colonialité du système scolaire au Canada et à valoriser la richesse du plurilinguisme ambiant. Language portraits are truly gateways to discovering an individual’s linguistic experience and identity. Starting with a pre-drawn silhouette or none at all, they allow individuals to visually represent the many languages in parts of that imagined body. Language portraits are therefore invaluable tools for (personal) reflection on the rich diversity of linguistic repertoires. In the field of teaching additional languages, especially English (Teaching of English as an Additional Language, “EAL”), language portraits are proving their worth in recognizing the plurilingualism of learners. However, language portraits seem to have even greater potential. This article offers to initiate a conversation about the decolonizing potential of language portraits as a pedagogical activity in EAL. Drawing on a pilot project with pre-teenager school learners on the French island of Guadeloupe, the authors discuss the critical importance of using language portraits in EAL to contribute to the deconstruction of the coloniality of the Canadian school system and the valorization of the richness of ambient plurilingualism.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".