Moisan, S., Hirsch, S., Éthier, M.-A., Lefrançois, D. (dir.) (2022). Objets difficiles. Thèmes sensibles et enseignement des sciences humaines et sociales. Montréal, Québec. Fides
Bibliographic record
Abstract
André Éthier et David Lefrançois porte sur les thèmes sensibles et leur place dans l' enseignement des sciences humaines et sociales.Il vise à mieux connaître et à mieux aborder les enjeux controversés en salle de classe.Différents thèmes sensibles y sont présentés, notamment le génocide des Premiers Peuples au Canada, le racisme, l'androcentrisme, la colonisation, la place des minorités, etc. L' ouvrage est destiné aux experts de l' éducation, au personnel enseignant ainsi qu'à toutes personnes qui s'intéressent à ces enjeux.L' originalité et la pertinence de cet ouvrage tiennent de la diversité des thèmes abordés ainsi que de la cohérence de ces textes regroupés en un seul recueil.
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 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.018 |
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".