Bibliographic record
Abstract
Toshio Hosokawa est l’un des compositeurs japonais actuels les plus joués au monde. Son langage musical est profondément marqué par l’esthétique japonaise, notamment par le gagaku. L’orgue à bouche shō, caractéristique de la musique de cour japonaise, est particulièrement important dans sa démarche compositionnelle. Cet entretien se focalise précisément sur le shō pour appréhender le rôle de ce dernier dans son oeuvre. Hosokawa a développé quelques-uns de ses concepts fondamentaux, tels que la notion de « matrice » ou de corporalité, au contact de cet instrument. Il a également composé une quinzaine d’oeuvres pour shō, seul ou avec d’autres instruments, en collaboration avec la joueuse de sho Mayumi Miyata (née en 1954). Ces différents aspects ont été abordés au cours d’un entretien en japonais que le compositeur nous a accordé en visioconférence le 19 février 2021.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".