The Evolution of Themes, Networks, and Intersections in <i>Canadian Theatre Review</i>
Bibliographic record
Abstract
This article looks at the use of themes in Canadian Theatre Review as a means of focusing attention on the different networks involved in creating and promoting performance in this place many call ‘Canada.’ It points to CTR’s early focus on making sure the right hand knew what the left hand was doing through issues focused on different regions of Canada. Starting in the mid-1980s, however, themes started to shift to building networks around common interests or conditions experienced by specific groups of Canadian theatre artists who were excluded from the larger Canadian theatre imaginary. Intersections were built around issues like feminism, gender and sexuality, ethnicity, racialization, and cultural difference. This coincided with new ways of thinking about how we make and receive performances, changes needed in institutional structures that support performance creation, and a shift from a literary view of play creation to more focus on the different arts of performance. The influence of new computer technologies and of the new discipline of performance studies opened up more intersections between networks of theatre practitioners in fields like historical re-enactment, science, health care, social justice advocacy, and gaming. Approaching its 200th issue, CTR is creating important intersections between practitioners concerned with social inequalities, racism and gender discrimination in theatre, public participation, environmental issues, and more. Most important, it is encouraging the development of tools that will ensure more control of performance institutions for a wider range of artists and audiences than ever before.
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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 | 0.044 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.040 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".