The infrastructural politics of post-pandemic theatrical performance
Bibliographic record
Abstract
INTRODUCTION In March 2020, theatres around the world shuttered their doors in the face of the Covid-19 pandemic. Although it was not the first time that theatres had closed to contain viral spread, the scale and length of the closure was unprecedented. Unable to gather in person, theatre companies beyond and across Canada cancelled productions and postponed entire seasons. In Canada's largest city, Toronto, 12,000 public performances were cancelled between March 2020 and March 2021, leading to an estimated loss of CDN$58 million in ticket sales, a decline of 17 million audience members, and “CDN$16 million in lost artist and production salaries/fees” (Toronto Arts Council 2021). While some organizations found the demands of creating performance under pandemic conditions untenable, many theatres began pivoting, deploying diverse strategies to present new work and engage audiences safely. Aside from the almost universal move toward forms of digital dissemination, which involved adapting existing plays or commissioning new works for virtual platforms, companies reduced cast and crew size; collaborated with other theatres; reimagined the functionality of theatre spaces; and moved to outdoor private and public venues. This chapter considers how the “pivots” made by Toronto theatres in response to the Covid-19 pandemic represent a significant infrastructural shift in how theatre is conceived, constructed and presented. The chapter consciously employs the word “pivot” – a term that became ubiquitous in North America during the pandemic – with the dual purpose of both celebrating the creativity and ingenuity of theatres and signalling the challenge of constant adaptation. Focusing on the first and second waves (March 2020– February 2021), while acknowledging that waves varied geographically according to a series of factors (including government policies and supports, public attitudes and access to vaccines, masks and other forms of personal protective equipment (PPE)), it identifies the wide variety of artistic strategies companies developed to foreground the infrastructural reappraisal that followed. As the realities of the first pandemic wave hit, companies had to rethink their infrastructure, not only in the turn from live to virtual presentations, but also as a means of guarding against the uncertainty of last-minute changes in public health restrictions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.048 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".