Bibliographic record
Abstract
Purpose To detail the changing nature of retail and service activity in Canada's downtowns and examine the role of business improvement areas (BIAs) in promoting downtown vitality. Design/methodology/approach The research is based on a combination of retail structural analysis and case study research. The structural analysis provides data on transitioning urban demographics and tracks retail and service activity sales change in Canada's major metropolitan downtowns. The case study reports an overview of findings from in‐depth research with the Downtown Yonge BIA. A small number of retail metrics are presented. Findings The paper highlights the significant suburb shift in retail activity across Canada's metropolitan areas and the associated challenges that this has resulted in for the downtown. The role of BIAs are outlined, and examined with reference to operation of the BIA concept within the downtown core of Canada's largest metropolitan market, Toronto. Research limitations/implications The research has been selective in focusing on the Downtown Yonge BIA, the experiences of BIAs across Toronto (and other Canada metropolitan areas) are likely to vary widely. Highlights the need to develop metrics to measure performance and compare BIAs. Practical implications The paper provides an interesting perspective on BIA strategies, with the selected metrics providing BIA managers and urban planners with a set of additional measures to assess BIA performance Originality/value The paper relates BIA planning to the development of performance metrics.
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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 | 0.000 | 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 | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".