Comment nouer « le propre et le commun » dans l’enseignement de la morale à l’école?
Bibliographic record
Abstract
En France,l’enseignement moral et civique (EMC), introduit par la loi de la refondation de l’École de 2013, vise à provoquer un questionnement sur les valeurs. Mais comment différencier les valeurs propres des valeurs communes configurant l’école de la République française, sinon en distinguant la morale d’inspiration déontologique et tendue vers un horizon d’universalité, de l’éthique qui relève d’un positionnement singulier ? Cet article se propose de faire droit à deux axes de construction du sujet, l’un vertical, hérité de l’idéal laïc des Lumières, et l’autre traversé par la subjectivité. La construction axiologique de l’élève est en effet nourrie par sa sensibilité, soubassement de la réflexivité morale. La question reste toutefois posée d’une formation des professeurs à la mise en place de dispositifs innovants, mais surtout, à l’interprétation herméneutique d’une identité professionnelle qui ne peut faire l’économie de l’éthique.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".