Les préoccupations éthiques chez des enseignants de l’ordre collégial : caractéristiques, points de repère et stratégies de résolution
Bibliographic record
Abstract
Peu de travaux empiriques ont été réalisés au Québec sur les questions d’éthique, notamment au niveau collégial. Nous avons réalisé une recherche sur les préoccupations éthiques des enseignants du réseau collégial francophone. Les données ont été recueillies auprès de huit groupes de discussion regroupant 63 enseignants dans des collèges publics et privés. Dans cet article, les résultats de cette recherche sont exposés : ils portent sur le point de vue des enseignants à propos des caractéristiques qu’ils attribuent à des préoccupations ou des dilemmes d’ordre éthique ainsi que sur les points de repère et les stratégies qu’ils utilisent pour résoudre ces dilemmes.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".