Bibliographic record
Abstract
Composer and musical pedagogue Gilles Tremblay made significant contributions to the development of musical composition in Quebec in the second half of the twentieth century. After studying at the Montreal Conservatory (Conservatoire de Musique du Québec à Montréal), he attended workshops at the Marlboro School of Music (Vermont) in the summers of 1950, 1951, and 1953. He lived in Paris from 1954 to 1961, where he enrolled in the piano studio of Yvonne Loriod, took analysis courses with Olivier Messiaen, attended workshops on Ondes Martenot, and received counterpoint lessons with Andrée Vaurabourg-Honegger. Tremblay attended the Darmstadt International Summer Courses in 1957 and 1960, and worked at the GRM (Groupe de Recherches Musicales) led by Pierre Schaeffer. Involved at this time with the networks of French new music, he frequently met with Pierre Boulez, Karlheinz Stockhausen, and Iannis Xénakis. In 1961 Tremblay returned to Quebec and was appointed professor of analysis and composition at the Montreal Conservatory, a position he occupied until his retirement in 1997. His courses at the Conservatory were inspired by Messiaen’s famous analysis class in Paris. Tremblay found connections between master works of Western music that linked the past to the present, from Gregorian chants to the polyphony of Guillaume de Machaut, Monteverdi, and Mozart, through to the twentieth century. His courses were extremely influential to two or three generations of composers in Quebec.
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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 | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".