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Record W3156607564 · doi:10.1177/14740222211007403

Brokering reflective spaces: Experiential learning in a summer opera program

2021· article· en· W3156607564 on OpenAlexaff
Kelly Bylica, Sophie Louise Roland, Laura J. Benjamins

Bibliographic record

VenueArts and Humanities in Higher Education · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsWestern University
Fundersnot available
KeywordsExperiential learningOperaContext (archaeology)MusicalAgency (philosophy)PedagogyReflection (computer programming)PsychologyReflective practiceSociologyVisual artsArtComputer scienceSocial science

Abstract

fetched live from OpenAlex

Formal music performance studies within university settings strive to prepare the next generation of performers and pedagogues for musical engagement beyond university. Yet literature suggests that these spaces of study do not always lead to a sense of readiness for potential professional worlds, due in part to a lack of opportunities for guided, in-depth, critical reflection that helps students connect theory and practice. This article articulates findings from a study that sought to consider the impact of deliberate opportunities for reflection in The Accademia Europea dell’Opera (AEDO), a university-affiliated summer opera intensive experiential learning program. Utilizing a communities of musical practice framework, researchers worked collaboratively to help participants engage in guided critical reflection as they developed high-level musical skills through rehearsals and performances. This article specifically considers the ways in which a ‘broker’ helped participants develop practices of reflection and personal agency both within and beyond this context.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.116
GPT teacher head0.321
Teacher spread0.205 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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