Bibliographic record
Abstract
Bruce Mather is a Canadian composer. He first studied music at the Royal Conservatory of Music in Toronto (1952–57) and at the University of Toronto (1957–59), where he obtained a music degree in 1959, studying piano with Earle Moss, Alexander Uninsky, and Alberto Guerrero, and composition with Godfrey Ridout, Oskar Morawetz, and John Weinzweig. Early in his career, Mather attended the summer festival of the Aspen Music School (1957–58). The festival became the site of a significant encounter for Mather; it was at the festival that he was introduced to Darius Milhaud, whose composition class he later registered in while studying in Paris. In addition to Milhaud’s class, Mather also enrolled in Olivier Messiaen’s class on music analysis. In 1960, Mather attended the Darmstadt summer courses where he met Pierre Boulez, whose orchestra-conducting classes he attended in Bâle (Switzerland) in 1969. Mather continued to alternate his study periods between France and America, until obtaining a masters degree from Stanford, California, in 1962 and a doctorate from the University of Toronto in 1967. In 1974, the composer’s encounter with the franco-Russian composer Ivan Wyschnegradsky (1893–1979) marked a turning point in his career. A prominent pianist, Mather recorded the composer’s piano music with his wife, pianist Pierrette Lepage (b. 1939) and adopted Wyschnegradsky’s microtonal system of composition, which continues to mark his personal style.
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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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.079 | 0.034 |
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".