Past, present and future revitalization trends in Canadian mid‐size city downtowns
Bibliographic record
Abstract
ABSTRACT The article is a critical review of the literature investigating the impact suburbanization has had since the mid‐20th century on the downtowns of Canadian mid‐size cities and the strategies deployed to revitalize these districts. It demonstrates that large city downtowns are more likely than their mid‐size city counterparts to enjoy conditions favourable to their success, hence the need to devise revitalization efforts tailored to the reality of mid‐size city downtowns. The article identifies revitalization strategies adopted over the last decades, which mostly failed to reverse the decline affecting these downtowns. It then concentrates on the present, and likely enduring, revitalization model, which emphasizes hospitality, recreation, culture, services, and walkability. The article refines the understanding of the differences between mid‐size and large city downtowns by concentrating on their specific dynamics and explores future revitalization options for mid‐size city downtowns. It discusses the present and likely enduring absence of alternatives to the present revitalization model and highlights its equity downsides as it challenges the downtown low‐income resident living environment .
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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 | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".