Expert Western Classical Music Improvisers’ Strategies
Bibliographic record
Abstract
The growing interest in musical improvisation is exemplified by the body of literatures evidencing the positive impacts of improvisation learning on the musical apprentice’s aptitudes and the increasing presence of improvisation in Western classical concert halls and competitions. However, high-level Western classical music improvisers’ thinking processes are not yet thoroughly documented. As a result of this gap, our research addresses the following question: What strategies are implemented in the course of performance by Western classical music improvisers? To answer this question, semistructured interviews were conducted with five internationally recognized Western classical music expert improvisers. Each participant improvised, and immediately afterward, a retrospective verbal protocol with subjective aided recall data collection strategy was used to elicit the improvising musician’s strategies. After transcription, the interviews were coded and analyzed using NVivo 10 software, with a mixed (i.e., combining inductive and deductive coding) category approach. Our data revealed 46 improvisation strategies that were subsequently organized into five categories: preplanning, conceptual, structural, atmospheric and stylistic, and real time. Pedagogical implications arising from these findings are that (a) learners should be guided toward implementing various strategies and (b) the capacity to switch from one strategy to another according to the circumstances should be promoted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".