Les compétences interculturelles dans la formation des enseignants : une réponse polyphonique à la diversité culturelle
Bibliographic record
Abstract
Cet article présente une recherche ethnosociologique en sciences de l’éducation analysée de manière collaborative et interdisciplinaire. La didactique des diversités y est discutée sur la base d’un terrain portant sur des enseignements à l’interculturel en contexte pluriel. Partant des observations et des analyses, cette étude interroge la notion de compétences interculturelles et propose des pistes pour la formation des enseignants. Celles-ci visent l’accompagnement des enseignants et des formateurs dans la mise en oeuvre de pédagogies participatives et innovantes. L’objectif est de sécuriser laprofessionnalisation des enseignants sur le terrain des questions vives en éducation et d’apaiser des situations parfois vives en contexte d’enseignement. La diversité culturelle s’avère un enjeu à la fois scientifique et éthique pour l’enseignement supérieur et la recherche.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".