Bibliographic record
Abstract
Some of Debussy's most beloved pieces, as well as lesser-known ones from his early years, set in a rich cultural context by leading experts from the English- and French-speaking worlds. The music of Claude Debussy has always been widely beloved by listeners and performers alike, more perhaps than that of any of the other pioneers of musical modernism. However rich in itself, his creative output also participated,and continues to participate, in a network of cultural connections, the scope and meaning of which can only be gleaned through multiple interpretive frameworks. Debussy's Resonance offers twenty new studies by some of themost active and respected English- and French-language scholars of French music. The book treats a large swath of the composer's music, from previously unexplored mélodies of his early years to late pieces such as the ballet Jeux and the Douze Études , and takes into consideration the numerous contexts that helped shape the works and the different ways that musicologists and critics have explained them. CONTRIBUTORS: Katherine Bergeron, Matthew Brown, David J. Code, Mark DeVoto, Michel Duchesneau, David Grayson, Denis Herlin, Jocelyn Ho, Roy Howat, Steven Huebner, Julian Johnson, Barbara L. Kelly, Richard Langham Smith, Mark McFarland, François de Médicis, Robert Orledge, Boyd Pomeroy. Caroline Rae, Marie Rolf, August Sheehy FRANÇOIS DE MÉDICIS is Professor of Music at the Université de Montréal. STEVEN HUEBNER is Professor of Music at McGill University.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".