Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".