Reconjuguer nos langues dans la pédagogie de la réconciliation au collégial?
Bibliographic record
Abstract
Cet article offre une réflexion à la fois micro et macro sur la place des langues dans la pédagogie de la réconciliation au collégial. Nous y traçons un parallèle entre la discrimination systémique envers les membres des Premières Nations et les Inuits sur le territoire québécois et les discours et postures entourant la réconciliation dans l’enseignement collégial. Les vulnérabilités, résistances, responsabilités et contributions linguistiques émergeant de rencontres entre Aînées Anishinaabeg et collaborateurs autochtones et francophones dans un collège situé en territoire ancestral non cédé Anishinaabeg sont présentées comme fondements éthiques de notre questionnement. Les résultats proposent des questions clés pour les pédagogues et gestionnaires de collèges. Notre synthèse démontre les possibilités et contraintes d’une pédagogie de réconciliation conjuguée par, pour, avec et envers les langues autochtones en contexte francophone.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.034 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".