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Record W4290989995 · doi:10.1177/1321103x221114613

Composing for students: Composers’ reflections on the process of creating educational music

2022· article· en· W4290989995 on OpenAlexafffundabout
Susan Mielke, Bernard W. Andrews

Bibliographic record

VenueResearch Studies in Music Education · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversity of Ottawa
FundersOntario Trillium Foundation
KeywordsMusic educationPsychologyActive listeningVariety (cybernetics)Composition (language)Process (computing)Musical compositionHarmony (color)TimbreContext (archaeology)Qualitative researchMathematics educationMusicalPedagogyComputer scienceVisual artsCommunicationSociologyLinguisticsArt

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.500
GPT teacher head0.536
Teacher spread0.036 · 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 teacher head, not a consensus.

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

Citations12
Published2022
Admission routes3
Has abstractyes

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