Composing for students: Composers’ reflections on the process of creating educational music
Bibliographic record
Abstract
The purpose of this qualitative study was to investigate the process of composing educational music. As part of a research project titled Sound Connections: Composing Educational Music Canadian composers completed email interviews, responding to semi-structured questions about the process of composing educational music. Using qualitative data analysis, we sought to understand better this process and found that the composers in this study used a variety of compositional techniques at all stages of the compositional process (conceptualizing, writing, and refining) to promote the development of student musicians. Key findings included the importance of balancing skills review and challenge; the use of form, timbre, and harmony for the development of communication and listening skills specific to the ensemble context; the use of elements from various musical styles to support music appreciation; and the encouragement of student collaboration in the creative process of composition. An unexpected finding was the importance of composer collaboration with teachers and students in the composing process. The knowledge gained in this study adds to the literature on this under-researched topic, and may help composers, student composers, and composition teachers develop a better understanding of and appreciation for educational music, thereby encouraging educational music composition. In addition, the findings of this study may assist teachers in the difficult task of choosing educational music for their students.
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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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".