Bibliographic record
Abstract
This research project, “Decolonizing Toronto Theatre,” examines how Soulpepper, a mainstream Toronto theatre company, and their collaboration with Native Earth Performing Arts are contributing to the equity, diversity, inclusion, and decolonization of Toronto theatre through their recent Indigenous productions: Kamloopa and Where the Blood Mixes. The author watched, read, and analyzed both plays to explore how these two productions transform and redefine the intellectual, political, and artistic conventions of Anglo-Canadian theatre. Her analyses of these plays are informed by the various texts centered around Canadian Indigenous history and Indigenous theatre. She also used an ethnographic approach by talking to people involved in both productions. She conducted interviews with the playwrights, the associate artistic director at Soulpepper, and some artists involved in both plays. These conversations with the people involved allowed her to understand these plays beyond their content: the inner workings of how a production comes to fruition. The conversations also allowed for a reflection on the similarities and differences between the creative approaches the artists involved took as well as the positive impacts these productions have had on Toronto theatre. Finally, by applying ethnographic findings and analyses of the plays, this piece compiles the analyses and research conducted over the course of the internship.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".