Bibliographic record
Abstract
Thom Sokolosky, directeur de la compagnie Autumn Leaf vouée à la production d’opéras contemporains, nous parle de sa plus récente production, Kopernikus de Claude Vivier. A travers son témoignage, il nous fait part de sa vision de l’opéra, de ses conceptions artistiques, de sa manière d’appréhender et de former une équipe qui réponde aux besoins spécifiques de chacune de ses productions. Ainsi, il est question des critères fondamentaux déterminant le choix de ses collaborateurs et de tous les participants, mais aussi de ceux qui régissent les associations, toute nature et tous niveaux confondus, entre artistes, entre organismes, villes, pays et même entre continents. Il interpelle également les subventionnées, interroge les enjeux politiques, les a priori artistiques et partage avec nous son voeu le plus cher : battre en brèche les limites qui étouffent, restreignent et empêchent. En un mot, et quel qu’en soit l’ordre ou le lieu d’application, faire preuve de la plus grande ouverture possible.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.015 |
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".