City Stages: Theatre and Urban Space in a Global City (Review)
Bibliographic record
Abstract
Michael McKinnie’s City Stages is a groundbreaking book, the first full-length text to treat the complex intersection of theatre, urban policy, socioeconomics, and political ideology in what McKinnie terms ‘global’ Toronto. At first glance such a topic might not seem especially revolutionary: after all, human geographers such as Edward Soja and David Harvey have been preoccupied with the vicissitudes of urban culture for more than two decades. What makes McKinnie’s text both unique and valuable is its avenue of approach: it understands the spaces of theatre – both its literal, physical spaces and its imaginary, creative spaces – as integral to civic politics and civil life, integral enough to warrant a specifically theatre-focused study of how Toronto has developed over the last half century into a city shaped by performance. In his comprehensive yet lively introduction, McKinnie lays out the research questions that drove his study along these very lines: ‘Was the calculus of how theatre in Toronto could be staged informed by assumptions of where it could be staged? Did the particular urban geography of Toronto itself play a part in theatrical production in the city? And, inversely, did theatre play a part in the urban development of Toronto?’
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".