Reviving London, ON: The Role of the John Labatt Centre and Covent Garden Market
Bibliographic record
Abstract
Societal changes have changed the function and presence of downtowns over the years and a variety of strategies have been implemented in an effort to revitalize downtown cores. One of the most recent strategies employed has been using urban catalysts, such as stadiums and markets, to stimulate downtown revitalization. The primary purpose of this strategy is to create catalysts for further development and investment. This study examines the role the John Labatt Centre (JLC, a recent arena) and Covent Garden Market (a farmers market with a large range of permanent food retailing facilities) play in revitalizing Downtown London, Ontario. \nWithin the last decade the City of London invested millions of dollars into rebuilding the Covent Garden Market and constructing the John Labatt Centre in the heart of London’s downtown. The purpose of this research is to determine whether these venues act as catalysts for new development, and thus assess their spin-off effects. \nData was collected by reviewing planning legislation, administering a survey to local business owners and interviewing key stakeholders. Findings show that the impact of the JLC and Market is unevenly distributed. The results provide insight on differences based on business type, and geographical location. \nPlanning implications derived from the London, Ontario case study show that continued commitment from the public and politicians is the most important factor in downtown revitalization. Implementing urban catalysts helps to anchor downtown districts, by providing a destination. However, this strategy needs to be applied in conjunction with innovative ideas, such as a Main Street program and incentive programs (façade improvements, waiving development charges on residential buildings) that instill confidence in the private sector.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".