Bibliographic record
Abstract
Les specialistes d'ethique de l'enseignement ont pris l’habitude de se reunir, depuis pres de dix ans maintenant, dans le cadre des rencontres du Reseau Education et Formation (REF). Ils ont eu l’occasion de faire connaitre leurs travaux avec deux ouvrages collectifs: Reperes pour l’ethique professionnelle des enseignants publie aux Presses universitaires du Quebec en 2009 (sous la direction de France Jutras et de Christiane Gohier) et L’ethique professionnelle des enseignants publie en 2012 chez L’Harmattan (sous la direction de Didier Moreau). Les etudes rassemblees dans le present volume sont issues du seminaire de Geneve des 9 et 10 septembre 2013. Elles prolongent et renouvellent de maniere originale les analyses presentees dans les precedents ouvrages, elles ont ete regroupees sous deux entrees intitulees « Perspectives ethiques » et « Perspectives de formation ». Mais celles-ci ne doivent pas etre apprehendees comme deux parties trop etanches car toute formation presuppose une conception ethique et, a l’inverse, toute perspective ethique laisse entrevoir des modalites de formation. Chaque contribution se termine par une bibliographie relativement breve et par un lexique qui explicite, en quelques lignes, les concepts majeurs du texte.
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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".