Bibliographic record
Abstract
Penser l’éthique enseignante, c’est présenter les principes moraux ou les vertus éthiques qui organisent et gouvernent cette activité professionnelle. L’éthique enseignante noue trois vertus : la justice, la bienveillance et le tact. C’est ce qui est présenté dans la première section de cet article. Dans les trois sections suivantes, la question éthique est examinée d’un tout autre point de vue. Il s’agit de présenter les écueils qui la menacent. Nous en mentionnons trois : le minimalisme, le paternalisme et le moralisme. Cet examen minutieux nous invite alors à dire que l’éthique enseignante ne doit être ni minimaliste (sans pour autant être une morale épaisse), ni paternaliste (sans pour autant minorer la dimension du soin et de l’attention), ni moraliste (sans pour autant oublier l’exigence d’exemplarité).
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".