Les performances managériales des maisons d’opéra Comparaisons internationales
Bibliographic record
Abstract
Dans cet article, nous tentons d’expliquer les performances des maisons d’opéra sur deux critères : autonomie financière et taux d’occupation des théâtres. Pour cela, nous proposons des quantifications des politiques artistiques, des politiques de production et des éléments principaux de l’environnement comme la capacité des auditoriums, les caractéristiques de l’offre et la force de la tradition lyrique. L’analyse statistique porte sur un échantillon de 62 maisons d’opéra. Elle montre que ce sont les facteurs d’environnement : taille des salles, densité de l’offre et tradition lyrique qui fournissent l’essentiel de l’explication. Ce sont les maisons les moins exposées et soumises à la tradition lyrique qui sont les plus performantes. C’est l’innovation plus que l’apprentissage qui explique les performances.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".