THE ROLE OF ARTS AND CULTURE IN MODERN CITIES: \nMaking Art Work in Toronto and New York
Bibliographic record
Abstract
Cities throughout the world currently are exploring ways that arts and culture \ncan serve as an economic engine, build name recognition and become a source of civic \npride through a mix of policy, branding, and economic development. I examine the \nrelationship between cultural policy and the increased presence of arts and culture on \nthe economic development agenda in Toronto and New York during the decade of the \n2000s. I hypothesize that New York is more driven by economic motivations, and that \nToronto’s interest lies in the brand building aspect of arts and culture in city building. \nThis dissertation is a comparative case study that investigates the increased \npresence of arts and culture in the economic development toolkits of Toronto and New \nYork over the decade. Archival and historical data, in addition to interviews with elite \nactors provide a rich cache with which to answer the thesis question. Through the use of \nagenda setting theory, I find ways that arts and culture have been integrated into policymaking \nand urban planning for economic development in each city. I observe that \nToronto and New York are building and facilitating cultural districts, attracting and \nretaining creative workers, and articulating economic arguments for arts and culture in \norder to generate revenues and secure government and private support. \nEach city underwent a shock during the early part of the decade. For Toronto, it \nwas the endogenous shock of amalgamation, and for New York the exogenous shock of \n9/11. In both cities, arts and culture were employed as a part of the economic \ndevelopment toolkit to revitalize decaying areas, attract residents and tourists, and \ndistinguish themselves from other cities. I find that each urban center used arts and \nculture extensively to create a cultural city in the case of Toronto, and to recreate a \ncultural city in the case of New York. Policy recommendations include utilizing \nresearch and strategic planning, building relationships and stakeholder partnerships \nacross policy domains and sectors, and focusing both on public good and economic \nbenefit when integrating arts and culture into economic development interventions
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".