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Record W3128336484 · doi:10.1177/0255761421990820

Teaching creative music in El Sistema and after-school music contexts

2021· article· en· W3128336484 on OpenAlexaffabout
Sean Corcoran

Bibliographic record

VenueInternational Journal of Music Education · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsQueen's University
Fundersnot available
KeywordsMusic educationAgency (philosophy)Creative briefPedagogyContext (archaeology)CreativitySingingCurriculumMusicologyMusicalPsychologySociologyVisual artsArtSocial scienceSocial psychology

Abstract

fetched live from OpenAlex

El Sistema music programmes have blossomed over the past decade, with the aim of fostering social development through intensive orchestral music instruction. Many scholars agree that creative music making can facilitate student agency development, increase a sense of belonging and promote creative expression by allowing students to bring their perspectives to the learning context. With these benefits apparent, it seems rational that El Sistema should incorporate creative music making into its curriculum. To build understanding of how creative music approaches function in some programmes, I used a multiple qualitative case study to examine eight teachers’ perspectives of creative music making within El Sistema and after-school music programmes in Canada and the United Kingdom. Findings revealed that teachers conceptualized creative music making as activities that develop agency through collaborative music creation, that have the benefit of creating a sense of belonging and that give students the opportunity to contribute to their community. Successful nurturing of creative music making seems to rely on connecting students to their wider community, which is achieved in part through incorporating students’ own musical tastes. Teachers’ experiences with creative music making in their own music education played a crucial role in preparing them to teach creative music.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.035
GPT teacher head0.287
Teacher spread0.252 · 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 designNot applicable
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

Citations29
Published2021
Admission routes2
Has abstractyes

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