Bibliographic record
Abstract
When we set out to “stage” the city, just whose version of the city are we staging? As metropolitan centres around the world adopt the thinking of Richard Florida, the urban-policy’ theorist behind the now-ubiquitous “creative city” paradigm, civic politicians, arts administrators and private investors alike have begun to make the connection among wealth, clout and the power of performance. Thanks to Florida, it should be happy days for theatre artists in “creative” cities everywhere; in practice, things aren't always quite so rosy. In practice, an event like Toronto's high-profile Luminato festival wins $22.5 million in provincial funding, while the Ontario Arts Council, benefactor to hundreds of culture workers around the province, must make do with less than a quarter of that pot (Taylor). Increasingly, “staging” the city means what real estate agents think it means: fluffing the town for the global tourist gaze, getting the façade ready rather than supporting the structural backbone of the place. But with Richard Florida now ensconced at the University of Toronto's Rotman School of Management and his “creative city” the talk of the town, what are the alternatives, in Toronto or beyond?
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".