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Record W2907439759 · doi:10.1177/0022429418820340

A Cross-Cultural Examination of Lifelong Participation in Community Wind Bands Through the Lens of Organizational Theory

2019· article· en· W2907439759 on OpenAlexaff
Roger Mantie, Leonard Tan

Bibliographic record

VenueJournal of Research in Music Education · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsLifelong learningIncentiveDiversity (politics)SociologyThrough-the-lens meteringPsychologySocial psychologyPublic relationsPedagogyPolitical scienceLens (geology)Engineering

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate participant involvement in community wind bands through the lens of organizational theory to inform the music education profession about community wind bands as an “expressive” voluntary association with potential for lifelong participation. Twenty-eight informants were drawn from three community wind bands in the United States and four community wind bands in Singapore. Overall, responses between U.S. and Singapore informants shared many commonalities. Informants from both countries desired musical opportunities that aligned with their interests (incentives and commitment), viewed their participation as defined largely by the ensemble-conductor relationship (formal structures), and preferred rehearsing and performing under the direction of a competent and respectful conductor (leadership and authority). The diversity of bands from which informants were drawn points to the importance of an environment that supports a range of interests for lifelong participation.

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)0.008
metaresearch head score (Gemma)0.008
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 score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.008
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.002
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 head0.190
GPT teacher head0.421
Teacher spread0.230 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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