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Record W2992264097 · doi:10.1386/ijcm.8.3.217_1

Grappling with inclusion: Ethnocultural diversity and socio-musical experiences in Common Thread Community Chorus of Toronto

2015· article· en· W2992264097 on OpenAlexaffabout
Deanna Yerichuk

Bibliographic record

VenueInternational Journal of Community Music · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChoirMusicalChorusMulticulturalismInclusion (mineral)Diversity (politics)Violin musical stylesCultural diversityPerceptionPsychologySociologyGender studiesVisual artsPedagogyAnthropologyArtLiterature

Abstract

fetched live from OpenAlex

Abstract This pilot research study explored ethnocultural backgrounds of choristers and their socio-musical experiences participating in Common Thread Community Chorus of Toronto, a community choir that actively pursues cultural inclusion through policies of musical and financial accessibility, as well as choosing repertoire of diverse cultures. A survey of choristers investigated how Common Thread members’ ethnocultural backgrounds informed their perceptions of their musical and social experiences and of the choir’s cultural diversity, working from the assumption that all people have ethnocultural backgrounds. Research findings reveal complex and diverse cultures when singers reflect on their own experiences, but choristers tended to reduce cultural diversity to race and language when thinking about the choir as a whole, suggesting that perceptions may be operating from a white normative centre. The results of this pilot research raise significant questions about multicultural education and cultural inclusion efforts within community choral practices in ethnically diverse urban environments.

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)0.004
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 score0.631
Threshold uncertainty score0.733

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.010
Scholarly communication0.0050.002
Open science0.0010.009
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.160
GPT teacher head0.314
Teacher spread0.154 · 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

Citations22
Published2015
Admission routes2
Has abstractyes

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