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Record W4389968057 · doi:10.5429/ij.v5i1.706

Teaching and Learning Popular Music in Higher Education Through Interdisciplinary Collaboration: Practice What You Preach

2015· article· en· W4389968057 on OpenAlex
Liz Przybylski, Nasim Niknafs

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIASPM Journal · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMusic educationEthosContext (archaeology)Popular musicSociologyPedagogyAutonomyEthnomusicologySpace (punctuation)PsychologyMusicalVisual artsArtComputer sciencePolitical science

Abstract

fetched live from OpenAlex

This article provides a contextualized explanation of an emerging strategy for popular music teaching and learning in higher education that the authors term Improvisatory Integrative Learning. This strategy coalesces around four themes from a Do-It-Yourself and Do-It-With-Others ethos: autonomy, play, peer learning, and peer teaching. To explicate the possibilities and pitfalls of teaching popular music in this way, the authors analyze the approaches taken in a co-taught university course integrating two perspectives: music education and ethnomusicology. The interdisciplinary collaboration became an investigative space for informal music learning approaches in a formal context, in which students improvised with creative composition. We explore not only how processes that are part and parcel of popular music learning can help improve productivity in a popular music classroom, but also the ways that improvisatory integrative learning can serve a diverse university student population by expanding interdisciplinary approaches to multiple kinds of subject matter.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, 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 score0.512
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.006
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.114
GPT teacher head0.337
Teacher spread0.223 · 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