Planners’ Perspectives on British Columbia’s Small and Mid-sized Downtowns: Strengths, Weaknesses, Challenges
Bibliographic record
Abstract
Downtown revitalization remains a key priority for planners working in communities across North America. In small and mid-sized cities, downtown decline and disinvestment has been particularly noticeable, long affected by the patterns of suburbanization, and more recently, by the lingering effects of the Covid-19 pandemic. This study, based on a survey of planners working in British Columbia, evaluates the state of downtowns in British Columbia’s small and mid-sized cities. These findings highlight the strengths of downtowns as broadband availability, civic events and street-oriented retailed, whereas the most pronounced and common weaknesses are the absence of post-secondary institutions, high-density housing and frequent transit. The findings also illustrate that strengths and weaknesses vary across the cases, accounting for variations in city size and regional contexts. Additionally, this study highlights the prospects and impediments of downtown revitalization into the future, with six major impediments.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".