Appropriation contrastée de la transparence à l’université. Une étude comparée entre la France et le Québec
Bibliographic record
Abstract
Les universités mobilisent des outils de gestion pour répondre aux attentes en matière de transparence. Les rares travaux de recherche consacrés à la transparence des universités la présentent dans une seule perspective de redevabilité en phase avec le New Public Management. Or, de nombreux travaux en sciences de gestion mettent en évidence une approche plus large et contrastée de la transparence, fondée sur sa contextualisation. L’étude de la littérature traitant de ce concept et l’analyse de 36 plans stratégiques d’établissements français et québécois permettent de distinguer un nouveau type de transparence jamais identifié dans les recherches antérieures portant sur ce contexte des universités, la transparence capacitante. © 2021 IDMP/Lavoisier SAS – Tous droits réservés
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".