The Casting and Makeup of Drama, Theatre, and Performance Studies in Canada: A Report on the Discipline by the Numbers (and Letters)
Bibliographic record
Abstract
Are we poised at the outset of a faculty hiring crisis in Drama, Theatre, and Performance (DTP) studies at Canadian universities? The question has been raised before, and perhaps at no time more frequently than in recent years as more students are entering graduate school and completing their degrees following the worst financial downturn in seventy-five years. Reactions range from malaise (“It was bad twenty years ago too”) or comparativism (“It’s bad in other disciplines, and in other countries too”) to economic determinism (“As the stock market goes, so too university hiring; things will get better”) and even panic (“After years of sending out job applications I’d be a fool to stay on the job market any longer”). But what do we really know about DTP graduation and hiring rates in this country? This quantitative report updates and deepens past attempts to analyze perceptions about DTP education by offering aggregated “personnel flow” and “student flow” statistics generated from current Canadian university tenure-stream DTP faculty and graduate student population data. Faculty data were gathered primarily from university DTP and English department websites, and graduate student data were gathered primarily from records held at the University of Toronto’s Graduate Centre for Study of Drama, the largest single source of DTP faculty in Canada.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".