Bibliographic record
Abstract
Mes premières rencontres avec Marie-Christine au début des années 80 s’inscrivent dans la dynamique de coopération avec l’équipe des sciences de l’éducation de l’Université de Genève pilotée par Pierre Dominicé. Cette coopération tournait autour de recherches avec la biographie éducative comme moyen de formation (Pratiques du récit de vie et théories de la formation, Cahier no 44, 1985, Section des sciences de l’éducation. Université de Genève, coordonné par elle-même et Matthias Finger). En 1988, je fus invité sur son jury de thèse, intitulé justement : Le sujet en formation. Ce dont je me souviens c’est d’une violente polémique, entre deux autres membres du jury : Franco Ferrarotti, qui soutenait l’approche résolument dialectique de la thèse; et Michael Huberman qui, tout en ayant une approche systémique de la production, diffusion et utilisation des connaissances, privilégiait le pôle social, plutôt que personnel. Grand moment de prise de conscience et d’entraînement aux affrontements violents que peuvent susciter les transitions paradigmatiques.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".