Bibliographic record
Abstract
Given that not every city can be an alpha city in today's global urban hierarchy, what options do beta cities such as Toronto or Chicago realistically have in the cultural economy? Put differently, if cultural capitals such as New York, London, Paris, and Tokyo play critically important roles in certifying and establishing new trends in theatre, fashion, and other cultural industries, how can beta cities compete? Recent research suggests that the spatial distribution of cultural industries strongly resembles one of urban hierarchy, where the institutions and infrastructure that support the production and diffusion of new products are largely concentrated in only a few world cities. The implication of this hierarchy is that there is a clearly defined top tier that lower ranked, beta, cities look to for inspiration as they seek to improve their standing. Comparative case studies of musical theatre scenes in Toronto and New York provide insights into an alternative functional perspective on urban hierarchies and the complementarities among cities. This approach makes a distinction between development and diffusion activities, thereby recognizing opportunities for beta cities as important sites for experimentation and innovation, supported by attributes that could be seen as unique (and localized) strengths in an increasingly global cultural economy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".