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World Musics and Cultural Diversity in the Music Classroom and the Community

2014· book-chapter· en· W2479877736 on OpenAlexaboutno aff
Yiannis Miralis

Bibliographic record

VenueOxford University Press eBooks · 2014
Typebook-chapter
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
FundersMichigan State University
KeywordsDiversity (politics)PedagogyContext (archaeology)Music educationCultural diversityQualitative researchSociologyMathematics educationPsychologySocial scienceAnthropologyGeography

Abstract

fetched live from OpenAlex

Abstract Qualitative research has been significantly used for investigating complex and multifaceted issues regarding cultural diversity and the use of world musics in the school and the community. This article explores such qualitative studies that occurred in the United States or Canada and were published from 1980 to 2011. Included are 38 studies, which are presented chronologically and alphabetically within the following six categories: (a) elementary school (Gr. 1-5); (b) middle school (Gr. 6-8); (c) high school (Gr. 9-12); (d) higher education; (e) an unclear or combined educational level (elementary-university); and (f) a community context. These studies focused on the rich and diverse views and experiences of teachers, students, and administrators with regard to cultural diversity and the teaching of world musics; examined exemplary teaching approaches, ensembles, and programs; explored innovative approaches in music teacher education; and brought attention to issues of cultural identity, culturally responsive teaching, and antiracial pedagogy.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.011
Scholarly communication0.0080.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.101
GPT teacher head0.212
Teacher spread0.111 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2014
Admission routes1
Has abstractyes

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