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Record W4410742356 · doi:10.1080/14613808.2025.2509963

‘A teacher by day and a performer by night’: performer-educator identity tensions in a graduate community of practice course

2025· article· en· W4410742356 on OpenAlexafffund
Aaron Hodgson, Laura J. Benjamins

Bibliographic record

VenueMusic Education Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsWestern University
FundersWestern University
KeywordsPerforming artsMusic educationIdentity (music)PedagogyPsychologyVisual artsMathematics educationArtAesthetics

Abstract

fetched live from OpenAlex

This article explores how participation in a community of practice course aided negotiation between performer-educator identities among graduate students professionally active as studio teachers. Studio teachers may face isolation due to limited opportunities for professional development and the secluded nature of one-on-one instruction, and overemphasis on vertical knowledge transmission within a master-apprentice model. Often engaged in performing and teaching as part of a broader, portfolio career, studio teachers may face tensions between their performer-educator identities. These tensions in identity may be mitigated through participation in a community of practice. Using a qualitative case study methodology, researchers used questionnaires, reflective assignments, interviews, and non-participant observations to investigate the experiences of 6 participants enrolled in a one-semester graduate course. Themes include the impact of participation in a community of practice, tensions in identity, and alleviation of tensions between identities.

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)0.017
metaresearch head score (Gemma)0.026
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 score0.035
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0350.026
Scholarly communication0.0130.009
Open science0.0040.021
Research integrity0.0050.008
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 head0.231
GPT teacher head0.556
Teacher spread0.326 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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