L’enseignement des sujets sensibles en contexte de diversités
Bibliographic record
Abstract
Le présent article vise à analyser les dimensions affectives au cœur des tensions éthiques, politiques, sociales et pédagogiques (Hirsch et Moisan, 2022) liées à l’enseignement des sujets sensibles au postsecondaire ainsi que les processus par lesquels les affects peuvent structurer le rapport aux diversités et orienter les stratégies pédagogiques. À partir de témoignages de membres du personnel enseignant et de la communauté étudiante du collégial recueillis dans le cadre d’un projet de recherche sur les stratégies pédagogiques pour l’enseignement des sujets sensibles en contexte de diversités, nos réflexions visent à mieux comprendre les effets de la circulation de nombreux registres affectifs en classe et leur influence sur les approches pédagogiques liées aux diversités et aux sensibilités.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".