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Record W4399762759 · doi:10.5206/notabene.v17i1.17181

Liberatory Praxis in Operatic Rehearsal Processes

2024· article· en· W4399762759 on OpenAlexaffvenue
Emma Yee

Bibliographic record

VenueNota bene · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicArtistic and Creative Research
Canadian institutionsMount Allison University
Fundersnot available
KeywordsPraxisAestheticsPsychologyVisual artsArtEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

As a young operatic performer-scholar, I have observed issues in the industry that I believe stem from opera’s complex, authoritative power structure and many of its leaders’ refusals to change this structure. In working towards rectifying these issues, I ask: How can the operatic rehearsal process, specifically, embrace measures of liberatory praxis and power disruption? Drawing on secondary literature regarding historical practices, philosophies of power, and liberatory praxis in music education, and a roundtable discussion I facilitated among opera practitioners on leadership and power structures, I developed a workshop in which I aimed to enable singers’ agency using alternative rehearsal methods. This workshop used a collaborative, democratic structure which honoured each participant’s expertise and prioritized discussions of the goals of the workshop and of each individual participant. Although some singers seemed uneasy with a collaborative structure of rehearsal as opposed to one with a central authoritative figure, my personal observations and a survey completed by workshop participants showed that singers gained agency through the employment of democratic, liberatory rehearsal praxis. Based on these results, I recommend that opera companies adopt a structure of democracy and collaborative discussion of personal and social praxis to expand singers’ agency. These results also suggest that continued study in singers’ praxis, agency, and power in operatic processes is needed.

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: none
Teacher disagreement score0.949
Threshold uncertainty score0.996

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.041
GPT teacher head0.298
Teacher spread0.258 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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