L’évolution de la structure de financement des organisations muséales : éclairage sur le rôle des endowment funds
Bibliographic record
Abstract
Rompant avec une longue tradition de financement public, certaines grandes institutions muséales européennes voient la part de leur financement privé augmenter considérablement. Cette évolution s’accompagne de la possibilité d’investir cet argent sur les marchés financiers à travers des endowment funds . L’objectif de cet article est de proposer un éclairage sur ce nouveau moyen offert aux musées de gérer leur capital financier. Pour se faire, nous examinerons le système muséal américain où les endowments sont la règle. Cette analyse nous permettra de comprendre ce que recouvre cette notion, d’appréhender ses modalités de mise en oeuvre et de saisir les bénéfices et les risques auxquels les musées européens s’exposent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.000 |
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 teacher head, 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".