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Record W2971828280 · doi:10.1177/0027432119855693

Engaging with Popular Music from a Cultural Standpoint: A Concept-Oriented Framework

2019· article· en· W2971828280 on OpenAlexaboutno aff
Nasim Niknafs

Bibliographic record

VenueMusic Educators Journal · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
Fundersnot available
KeywordsPopular musicRepertoireTransformative learningMusic educationSociologyMusic GeographyDiversity (politics)MusicologyPopular culturePedagogyMusic historyAestheticsMedia studiesVisual artsArtLiteratureAnthropology

Abstract

fetched live from OpenAlex

The last two decades in the North America have seen a resurgence of scholarly and practitioner activities advocating for integrating more popular music in music classrooms both through repertoire and pedagogy. However, the emphasis has been on Western-oriented popular music practices, neglecting those of other cultures, even though there is a major increase in population diversity occurring in the United States and Canada. This article examines the concept-oriented framework, through which, instead of exploring popular music geographically, one can engage with the wider concept of popular music across cultures. The framework consists of a central concept selected by music teachers and students and is contextualized by various areas of exploration. Popular music can play an influential, transformative, and socially just role in improving numerous situations.

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)0.014
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.004
Science and technology studies0.0100.087
Scholarly communication0.0210.017
Open science0.0040.011
Research integrity0.0050.006
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 head0.038
GPT teacher head0.244
Teacher spread0.207 · 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 designTheoretical or conceptual
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

Citations24
Published2019
Admission routes1
Has abstractyes

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