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Record W4387219963 · doi:10.1177/02557614231188127

The many ways of Puerto Rican community music

2023· article· en· W4387219963 on OpenAlexaff
Francisco Luis Reyes, Lisa Lorenzino, Bronwen Low

Bibliographic record

VenueInternational Journal of Music Education · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsMcGill University
Fundersnot available
KeywordsIndigenousPromotion (chess)Music educationMusicalCommunity buildingSociologyPolitical sciencePublic relationsVisual artsPedagogyArt

Abstract

fetched live from OpenAlex

For several decades, scholars in the field of community music have largely concentrated on community music practices in the Global North. Such interest has not been as prevalent in certain parts of the world, like the Caribbean. Consequently, this qualitative multiple case study focuses on three Puerto Rican community music initiatives that foster the country’s indigenous music: Bomba, Plena, and Música Campesina. Scholars have documented the evolution and characteristics of these musical traditions. In contrast, this article centers around the practices of Taller Tambuyé, a female-led Bomba organization, Decimanía, a national Música Campesina initiative that funds other community music projects, and La Junta, a community-based project tied to the sector of El Machuchal in the capital of Puerto Rico. This paper presents and analyzes their practices through the lens of Australia’s Sound Links project and its nine domains of community music. The multiple case study methodology’s cross-case analysis revealed notable divergences among the projects in terms of learning practices, promotion of the indigenous music tradition, and the connection between the musical initiative and their community. Additionally, researchers found the framework established by Sound Links to be a comprehensive tool to analyze community music practices outside of Australia.

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.103
GPT teacher head0.280
Teacher spread0.176 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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