Arts in the City Downtown Revitalization Strategies for Mid-Sized Cities
Bibliographic record
Abstract
Bringing life back into the centre of smaller cities is possible through community-led arts and culture events. This study of community and artist-led events in Barrie, Kitchener and Hamilton in Ontario suggests that these types of place-making events should be supported by smaller cities as important catalysts to downtown revitalization. \n \nIn recent decades, cities of all sizes have attempted to revitalize the downtown via a myriad of approaches and strategies. Large cities are typically more successful in downtown revitalization in comparison to smaller urban centres. This research examines downtown revitalization strategies and determines that place-making approaches that emphasize arts and culture are best suited for smaller city centres. \n \nThe City of Barrie served as the primary case study for this research. A proposal for the City of Barrie was drafted to assist in downtown revitalization efforts (see Appendix C). In order to understand the options available for the City of Barrie, the City of Kitchener and the City of Hamilton were examined and served as methods of best practice. The lessons learned from the City of Kitchener and the City of Hamilton have been considered in my proposal for the City of Barrie. \n \nThis research has revealed that smaller cities should employ place-making strategies that emphasize the arts and culture sector in order to enhance the urban fabric. Based on comparable precedents, small scaled and strategic projects prove to be more economically feasible in comparison to costly large scaled projects. Place-making strategies result in greater economic spin-offs, facilitate community engagement, foster civic pride and advance the city's prosperity.
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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 | 0.013 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".