Reading the Theatre: A Lens-Based Method for Understanding Theatre Companies and their Facilities
Bibliographic record
Abstract
This dissertation proposes a new way of reading and understanding theatre companies that operate their own theatre facilities (called venued theatre companies) operating in cities. This method utilizes a set of nine lenses that will dissect both a theatre company and its venue into parts that can be examined and discussed separately. These lenses are divided into three areas: Company, Building and Relationships. These areas are similarly divided into separate areas. The companies themselves are examined in terms of their History, Mandate and Operation. The theatre buildings/facilities they create and operate are examined in terms of Site, Stage, Front-of-House, and Infrastructure. A company’s relationships are considered in terms of those with its Audiences, and those with the City around it. These lenses are then applied to four not-for-profit theatre companies and their venues in Toronto: Theatre Passe Muraille at 16 Ryerson Avenue, Buddies in Bad Times at 12 Alexander Street, the Theatre Centre at 1115 Queen Street West, and Crow’s Theatre at 345 Carlaw Street. Each analysis delivers an extensive organizational and architectural history of the company and building that explains how these facilities came to be and work as they do. They will also outline the implications of choices made in theatre design and operation for the company, and for the theatre and urban ecologies around it. The dissertation continues with an analysis of what is learned from the four examples taken together, what can be understood about the construction and design of theatre facilities, and about the operations of venued theatre companies in the city of Toronto. It will conclude by discussing the implications of the COVID-19 pandemic for these companies and for theatre in general, and what it could mean for the future of theatre production in the city of Toronto.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".