Les musées de l’école et de l’éducation : un champ muséal quantitativement significatif mais difficile à cerner
Bibliographic record
Abstract
En étudiant la gestion, les politiques d’acquisition, de documentation et de valorisation d’un bon nombre d’institutions muséales européennes vouées à l’école et à l’éducation, Myriam Boyer établit une réflexion sur la pérennisation de leurs collections et sur les enjeux liés à leur diffusion. Fondé sur une étude de terrain rigoureuse menée entre 2005 et 2007, le panorama quantitatif et qualitatif qu’elle a dressé lui permet de poser une réflexion démontrant qu’une corrélation peut être établie entre les missions de ces musées et l’esprit du temps d’une société. Pour l’auteure, la sauvegarde de ce patrimoine repose sur deux conditions : la reconnaissance du rôle patrimonial et social de ces collections et le ralliement et la diversification des publics.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".