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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".