Bibliographic record
Abstract
Cet article procède de la philosophie politique et de l’éthique interdisciplinaire de l’éducation et de la formation. Il questionne ce qu’il peut s’agir au juste d’« élever » chez l’enfant quand on le qualifie, dans sa scolarisation, d’élève, et la part qu’y tient l’idée de perfectionnement moral. Nous montrons d’abord que l’oeuvre durkheimienne renferme des ressorts compréhensifs précieux pour comprendre une conception maximaliste de l’éthique de l’élève - elle-même inscrite dans une perspective perfectionniste – aux sources de l’école républicaine en France, et qui conserve une influence aussi durable que désormais problématique (1). Nous dégageons ensuite les linéaments d’une éthique minimaliste de l’élève à même d’en prendre le contrepoint, et tâchons de montrer qu’elle est plutôt en congruence avec le cadre axiologique des démocraties libérales avancées (2). Une ouverture conclusive nous permet enfin d’esquisser quatre sauf-conduits possibles pour nous aider à échapper, sur ces bases, à l’héritage paternaliste du concept d’élève.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".