Creative Toronto: Harnessing the Economic Development Power of Arts and Culture
Bibliographic record
Abstract
abstract: Over the 2000s, Toronto initiated and instituted a process of cultivating itself as a creative city. Entrepreneurial city visionaries found that in order to enter the global market, their planning had to be strategic. This paper explores how Toronto’s policy entrepreneurs used planning, partnerships, and an expanded definition of economic development to create a “Cultural Camelot.” In addition to competing on the financial and revenue-generating fronts, a coalition of cross-sector leaders took on the challenge of fostering a livable city with a deep social ethos imbued within a variety of dimensions of urban life. This new focus gave Toronto the chance establish itself as a center for innovation, which strengthened urban cultural capital and helped promote the strategic agenda of becoming a competitor in the creative economy sector. Investment in research and creative city strategic planning, coupled with the allocation of financial and human capital resources across a variety of industries, served to encourage creativity, promote culture and competitiveness, and drive economic development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".