Pour une conceptualisation de la compétence multiculturelle de l’enseignant
Bibliographic record
Abstract
Dans un contexte de professionnalisation des enseignants, la compétence multiculturelle bénéficie d’un statut ambigu et controversé autant dans les discours gouvernementaux que dans la pratique enseignante. En s’appuyant sur des formalisations récentes du concept de compétence, cette étude vise à cerner le sens et le statut de la compétence multiculturelle de l’enseignant. Menée auprès de neuf étudiants-maîtres francophones, cette recherche exploratoire utilisant une méthodologie qualitative interprétative propose une définition de la compétence multiculturelle de l’enseignant et discute du statut à lui accorder parmi les autres compétences professionnelles. Enfin, des suggestions de développement de ladite compétence pendant la formation initiale des enseignants sont formulées. In the context of teacher professionalization multicultural competence receives an ambiguous and controversial status both in the government discourse and in teaching practice. Based on recent formalizations of the concept of competence, this study aims at defining the meaning and status of teachers’ multicultural competence. This exploratory research carried out with nine francophone student teachers used an interpretive qualitative methodology to propose a definition of multicultural competence for teachers, and discusses the status it should be granted among other professional competences. Suggestions are provided on how to devleop this competence during initial teacher education.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.046 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".