The Career Profiles and Educational Activities of Ten Canadian String Quartets
Bibliographic record
Abstract
This paper provides a focused look at the career profiles and educational activities of ten Canadian string quartets, concentrating on how they divide their time between performing, teaching, recording, outreach and personal projects. It examines the collaboration between string quartets and larger organisations such as orchestras or educational institutions, and explores what is contracted work and what is self-initiated by the quartets. This paper asks and answers the question, what do Canadian string quartets do besides classical music concerts, and why is it important. Ten groups, five established and five emerging, are examined through the process of interviews and surveys to delve into these issues. This paper begins with a detailed look at the outreach and educational undertakings of many important historical Canadian quartets in the twentieth century. In addition, it introduces the ten Canadian string quartets, providing biographical information about their performing career, notable recordings, and educational posts. Three other sections follow, the first detailing the emerging string quartets singularly, then a comparison of their pursuits. Secondly, a detailed look at the established string quartets one-by-one, and a section which contrasts them. Finally, both emerging and established are examined for connections and similarities as well as distinctions and differences.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".