Organizing Canadian theatre designers: The intersection of creative and precarious labour
Bibliographic record
Abstract
Canadian theatre designers share many similarities with other freelance, creative workers in Canada. The conditions of precarity that define their working relationships are similar to those that affect workers in other sectors, such as film, music, television, and visual arts. This thesis begins by examining the existing literatures and research concerning creative and precarious work, primarily in Canada, but also internationally. Drawing on in-depth interviews of 55 designers from within the relatively small community of Canadian theatre designers, approximately 500-700 workers, I examine the working conditions that designers find challenging and seek suggestions for how they can be improved. Additionally, I explore the different models that designers have used to organize in Canada, Quebec, and the United States. By comparing these models with the interviews from designers, I conclude that the best way for Canadian designers to improve their working conditions is to build a closer relationship with IATSE, the union that represents stagehands and technicians. Finally, I identify some questions for further exploration, including the tension between artistic and worker identities, while also touching on the present circumstances of the Covid-19 crisis and the current conversations concerning racism and white supremacy within Canadian society.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.070 | 0.038 |
| Scholarly communication | 0.021 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".