"But You're a Violinist - Why Do You Compose?": Narratives of Experience of Three Composer-Performers
Bibliographic record
Abstract
In the past century, a perception has arisen of a decline in the practice of musicians who both compose and perform. Classical musicians, such as Frédéric Chopin, Maurice Ravel, and Camille Saint-Saëns, historically emulated their mentors by composing alongside maintaining a performance career. However, present-day musicians have become increasingly specialized in the fields of performance and composition; those that continue to pursue both simultaneously can now be defined specifically as a "composer-performer," a term that was irrelevant in the past, as both composition and performance were already integrated in a musician's career. The purpose of this study is to explore the definition of "composer-performer" in contemporary music careers. The second objective of this study is to begin a discussion on why and when the career paths for performers and composers became increasingly divided, exploring possible key factors that may have motivated the specialization of composers and performers. The last objective is to provide a platform for the voices and stories of modern-day composer-performers. In this study, the experiences, philosophies, and challenges shared by composer-performers participating in the research process will be discussed. This study utilizes narrative methodology in order to tell the stories of contemporary composer-performers first-hand, and to represent their experiences in their own voices. Conversational interviews were conducted with two composer-performers, their narratives analyzed and themes categorized. The resulting data was put into conversation with themes and perspectives data extracted from my personal narrative on my experiences as a composer-performer. This study provides insight on the dual role of the modern composer-performer in a society that rewards specialization. It also proposes questions for future research.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.040 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".