Identifier les besoins en éthique professionnelle enseignante : une recension des écrits
Bibliographic record
Abstract
Cet article présente une recension des écrits scientifiques concernant les besoins en éthique professionnelle du personnel enseignant des niveaux scolaires allant du préscolaire au collégial. À partir du cadre conceptuel « québécois » de l’éthique professionnelle enseignante et d’une méthodologie de recension appropriée, nous recensons 61 écrits scientifiques, publiés entre 2003 et 2021, qui discutent de ces besoins, c’est-à-dire des ressources qui manquent aux enseignants et enseignantes pour respecter une éthique professionnelle souhaitable. La première partie de cet article expose les résultats de ce travail de recension. Puis, dans un deuxième temps, la discussion aborde la difficulté d’établir un cadre conceptuel adéquat pour analyser la diversité et la particularité des besoins ainsi recensés.
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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 | 0.035 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".