Bibliographic record
Abstract
В беседе с Е. Купровской французский композитор Филипп Леру вспоминает о своей работе в IRCAM, о царящем там творческом духе. Композитор также рассказывает о процессе сочинения музыки, который трактует как предварительное «ви́дение» звукового потока и придание ему энергетической, или морфологической, логики, и объясняет свой повышенный интерес к буквам как концептуальной основе музыкального сочинения.. Леру делится своим опытом преподавания композиции и выражает оптимизм по поводу социального положения композитора во Франции. In a conversation with E. Kouprovskaia, the French composer Philippe Leroux recalls his work at the IRCAM, and the creative spirit that reigns there. The composer also talks about the process of composing music, which he interprets as a preliminary “vision” of the sound stream and giving it an energetic, or morphological, logic, and explains his increased interest in letters as the conceptual basis of musical composition. Leroux shares his experience of teaching composition and expresses his optimism about the social position of a composer in France.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".