Creating Toronto’s Cultural Camelot and Reviving New York’s Golden Apple
Bibliographic record
Abstract
Toronto&s;s focus on arts and culture centered on its aspiration to enter the international market of the knowledge economy, implemented in part through the city&s;s cultural renaissance. In Toronto, there was a relationship between cultural policy and economic development&s;s use of arts and culture on its agenda and in its toolkit. Arts and culture played an important role in contributing to the economy of New York in the decade, and issues going forward include building demand and providing more value to city residents through arts and culture. The differences in the two cities can be accounted for by looking first at the way that New York already was a cultural and creative center, while Toronto was aspiring to become one. There are several factors that account for differences between these two cities in the area of leadership and partnership building over the decade, including New York&s;s history of private involvement in the leadership area of arts and culture.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 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".