Canadian Performance Documents and Debates
Bibliographic record
Abstract
Canadian Performance Documents and Debates provides insight into performance activities from the seventeenth century to the early 1970s, and probes important yet vexing questions about Canada as a country and a concept. The volume collects playscripts and archival material to explore what these documents tell us about the values, debates, and priorities of artists and their audiences from the past 400 years. Analyses throughout rethink the significance of theatre, dance, opera, circus, and other performance genres and events. This landmark collection challenges readers to reconsider Canadian theatre and performance history. Foreword by Jerry Wasserman. Contributors: Clarence S. Bayne, Kym Bird, Justin A. Blum, Amy Bowring, Jill Carter, Jenn Cole, Cynthia Cooper, Heather Davis-Fisch, Moira J. Day, Ray Ellenwood, Alan Filewod, Howard Fink, Liza Giffen, J. Paul Halferty, James Hoffman, Erin Hurley, John D. Jackson, Stephen Johnson, Sasha Kovacs, Sylvain Lavoie, Louis Patrick Leroux, Allana C. Lindgren, Denyse Lynde, Erin Joelle McCurdy, Wing Chung Ng, Glen F. Nichols, M. Cody Poulton, VK Preston, Daniel J. Ruppel, Jordan Stanger-Ross, Paul J. Stoesser, Christl Verduyn, Anthony J. Vickery, Anton Wagner
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.027 | 0.006 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.010 |
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".