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Record W4255199575 · doi:10.1386/jpme_00004_1

Learning through praise: How Christian worship band musicians learn

2019· article· en· W4255199575 on OpenAlexaff
Laura J. Benjamins

Bibliographic record

VenueJournal of Popular Music Education · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsWestern University
Fundersnot available
KeywordsPraiseWorshipMusic educationMusicalChristian musicAestheticsPopular musicContext (archaeology)ElitismExcellenceSociologyInclusion (mineral)PsychologyVisual artsPedagogyArtSocial scienceSocial psychologyEpistemologyHistoryTheologyPolitical sciencePoliticsPhilosophy

Abstract

fetched live from OpenAlex

Popular music education continues to increase in North American educational settings. While popular music teaching and learning are recognized in a variety of contexts, contemporary Christian church praise bands have not been significantly addressed in music education literature. In addressing this gap, the purpose of this study is to examine the musicking practices occurring in the contemporary worship music (CWM) context and how these lead contemporary Christian musicians to acquire and develop their musical skills. Green’s five principles of informal music learning were found to apply in part, yet other distinctive features were also present in study findings. Themes such as elitism, excellence, hierarchies of musical engagement, and inclusion/exclusion of worshippers and the congregation also arose, providing interesting areas for future research.

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)0.006
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 score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.007
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.048
GPT teacher head0.257
Teacher spread0.210 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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