Quelle place accorder aux thèmes sensibles dans l’enseignement d’Éthique et culture religieuse au Québec ?
Bibliographic record
Abstract
Dieser Artikel fokussiert auf den Unterricht heikler Themen, die mit Religion im québec’schen Lehrplan des Faches „Ethik und religiöse Kultur“ (éthique et culture religieuse, ECR) verbunden sind. Es wird analysiert, wie die dazugehörigen Lehrmittel und Schulhefte, die zu diesem Curriculum gehören, die heiklen Themen behandeln. Auch wenn der Lehrplan an wenigen Stellen die Problematik dieser Themen anerkennt, haben die Lehrpersonen die Tendenz, diese nicht zu thematisieren, da sie einen möglichen Aufruhr vermeiden wollen, welcher Diskussionen in der Klasse verursachen könnte. In welchem Ausmass können die Lehrmittel die Lehrpersonen in ihrer Unterrichtsvorbereitung dahingehend unterstützen?
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".