Canadian Actors’ Equity: Recognize What Is “Canadian” about Theatre Practice in This Country
Bibliographic record
Abstract
I was amused, recently, to hear of the conflict, in Toronto in January 2005, between Canadian Actors Equity and The Blue Man group. Equity took full advantage of the situation to differentiate itself from its American counterpart. Executive Director Susan Wallace pointed out that Canadian Actor’s Equity and American Actor’s Equity are very different organizations: “It’s absolutely a different playing field here in Canada,” she said; and “They’re coming into a new community that has new standards” (Ouzounian). I am not so certain that is true. Canadian and American Actor’s Equit[ies] may quibble on certain fine points, but the fundamental philosophies that govern these organizations are frighteningly similar. And though Canadian Actor’s Equity has made some headway during the last twenty years in understanding the differences between Canadian and American theatre, I think it is important to know that they have a regrettable history of ignoring those differences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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; both teacher heads agree on what is shown here.
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".