Casting Audiences: How Theatre Passe Muraille’s ‘Black Out Nights’ Challenge Conventional Approaches to Audience
Bibliographic record
Abstract
This article examines the phenomenon of Black Out Nights, evenings of a theatrical performance for Black-identified audiences only, as employed by Theatre Passe Muraille in Toronto. Drawing on interviews with Passe Muraille’s former Marketing Coordinator Fatuma Adar and current Artistic Director Marjorie Chan, this article explores the various effects and benefits of Black Out Nights, which were first conceived by American playwright Jeremy O. Harris, for Black artists and audiences. I argue that Black Out Nights, along with Theatre Passe Muraille’s other approaches to audiences, help to reveal and to challenge larger practices of ‘casting’ audiences that are employed by mainstream theatres. These casting practices, which operate through theatres’ websites, marketing, and theatre spaces, may be unconsciously exercised but help to reinforce white supremacy and other forms of discrimination and exclusion in theatre spaces through the audiences they make feel welcome and exclude.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".