Extending Vocal Pedagogy: Extended Vocal Techniques in North American Post-Secondary Music Education
Bibliographic record
Abstract
In North America, post-secondary music education is heavily focused on (and limited to) the repertoire and techniques of the Western Art Music canon. Vocal training at these institutions is no exception: vocalists are trained in the bel canto technique whose lineage reaches back to seventeenth-century Italy. This conservatory-based curriculum supports a categorical vocal pedagogy, one that seeks to produce a particular type of singer with a very specific kind of sound. Instead of embracing what each individual singer is capable of, this model focuses on what singers should be capable of from the perspective of repertoire and technical mastery in the operatic tradition. In this paper I will argue that this model risks our losing sight of what the singer has to say in favour of what the composer has to say. Recently there has been discussion and research around a more inclusionary model of vocal pedagogy that would incorporate other techniques alongside bel canto. However, these discussions have been focused on inclusion of musical theatre and belt techniques, with very little discourse on the inclusion of extended vocal techniques. By drawing on the scholarly discourse on the limits and extensions of technical training in post-secondary vocal performance, as well as interviews with several women working in the performance and teaching of extended vocal techniques in Canada, I will explore the potential for extended vocal techniques to contribute to a more inclusive model of vocal pedagogy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".