À propos de la condition enseignante : au-delà des difficultés, des gains et des pertes, il importe de renouveler le sens du métier pour le rendre plus attrayant
Bibliographic record
Abstract
Cet article aborde la condition enseignante, définie comme réalité multidimensionnelle, objective et subjective, ayant valeur de symbole et objet de politiques publiques. Souvent traitée globalement et en termes de gains et pertes, celle-ci est analysée à l’aide de deux distinctions : a) entre deux types de professionnalisation, comme mouvement porté par les enseignants et comme politique d’État ; b) entre deux orientations, l’une axée sur la qualité de la main-d’oeuvre enseignante (politique d’État) et l’autre soucieuse de l’organisation professionnelle des lieux de travail (revendiquée par les enseignants). Ce second type de professionnalisation doit être priorisé, même s’il s’oppose à la gestion axée sur les résultats (GAR). Enfin, recomposer la figure de l’enseignant serait une stratégie gagnante pour revaloriser le métier et le rendre plus attrayant.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".