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Record W2964625790 · doi:10.1386/jpme.3.2.203_1

How does a rock musician teach? Examining the pedagogical practices of a self-taught rock musician–educator

2019· article· en· W2964625790 on OpenAlexafffundabout
Julia Brook, Robbie MacKay, Chris Trimmer

Bibliographic record

VenueJournal of Popular Music Education · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPersonhoodContext (archaeology)DispositionPedagogyFrame (networking)SociologyPsychologyMathematics educationEpistemologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

This research examines the pedagogical practices of a self-taught musician who teaches music at an elementary school in Canada. Research on the ways that popular musicians teach has shown that many teachers use a combination of informal and formalized structures. We used Personhood theory as a conceptual framework to illuminate how the context and disposition of the musician–teacher informs their pedagogy. These findings demonstrate how context and disposition inform pedagogical practices and the ways that the teacher’s personhood contributes to students’ learning. We collected data through interviews with the teacher and school principal, distributed questionnaires to students and observed performances. Findings show that one’s personhood can contribute to the medium, message and messenger within a music education setting. Personhood theory helps frame the nested nature of these relationships and these findings point to the need to support development of in-service and pre-service teachers’ personhood.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.007
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.108
GPT teacher head0.309
Teacher spread0.201 · 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

Citations1
Published2019
Admission routes3
Has abstractyes

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