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Record W3047498394 · doi:10.25071/1916-4467.40496

Composing Together II: The Development of Musical Ideas with Teachers and Students

2020· article· en· W3047498394 on OpenAlexaffvenueabout
Tessandra Wendzich, Bernard W. Andrews

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsActive listeningMusicalMusic educationMusical compositionMusical developmentComposition (language)PedagogyPsychologyVisual artsMathematics educationArtLiteratureCommunication

Abstract

fetched live from OpenAlex

Contemporary Canadian pieces are uncommonly performed and studied in school music programs due to their complex nature. The Ottawa-Carleton District School Board and the Canadian Music Centre commissioned composers to write a piece of educational music in a multi-year, multi-site research project entitled Making Music: Composing with Young Musicians. The musical pieces were written in collaboration with teachers and students. The following research question was addressed: How can musical ideas be conceptualized and developed with students and teachers? Through composition reports, the composers indicated the importance of listening to students. Listening enabled them to know with what the young musicians were familiar and helped the composers discern the students’ instrumental abilities. Musical ideas were also developed when students worked individually and in groups. Furthermore, composer-teacher feedback as well as teacher facilitation spawned a healthy flow of musical ideas. The findings will be of interest to music teachers, post-secondary music educators, composers and Canadian music publishers.

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)0.005
metaresearch head score (Gemma)0.007
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 score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0120.007
Scholarly communication0.0120.003
Open science0.0010.006
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.055
GPT teacher head0.273
Teacher spread0.219 · 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
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueJournal of the Canadian Association for Curriculum StudiesSame topicDiverse Music Education InsightsFrench-language works237,207