Does Canada Need Graduate Training for Musical Theatre Creators?
Bibliographic record
Abstract
Musical theatre is big business in Canada, contributing to our culture and economy. However, Canadian musical theatre creators (composers, lyricists, book-writers, musical directors, and directors of musicals) often leave the country to acquire their skills and pursue opportunities in the industry. Canada does not offer official diploma, degree, or graduate programs for musical theatre creators. My project assesses whether Canada needs such a training program at the graduate level. I collected data through three methods: a survey for professional musical theatre creators; a survey for pre-professional musical theatre creators; and focus groups for musical theatre creators of varying experience levels for cross-case analysis. Surveys were analyzed, cross-tabulated, and then triangulated with the coded focus group data. My analytical approach combined Carliner's ADDIE framework and Mezirow's critical reflection theory, as well as examining data through a phenomenological lens, and I found there was support for a graduate program among practitioners as well as systemic issues, including the existence of misperceptions of musicals and the artists who create them in the general public. Further research might address how audiences, funding bodies, theatre companies, and policymakers view these artists; measure the cultural and economic impact of such artists; and hopefully improve their ability to train in Canada and make a living.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".