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Record W3155850943

The Career Profiles and Educational Activities of Ten Canadian String Quartets

2019· dissertation· en· W3155850943 on OpenAlexaboutno aff
Gwyneth Rebecca Thomson

Bibliographic record

VenueTSpace · 2019
Typedissertation
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
Fundersnot available
KeywordsString (physics)Mathematics educationPsychologyPhysicsTheoretical physics
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0170.003
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.038
GPT teacher head0.278
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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