Bibliographic record
Abstract
This study explores the evolution of the neighbourhood commercial landscape in Toronto. First, we review the major character and historical changes of Toronto’s commercial landscape, most notably Queen Street and Kensington Market; how they emerged and how they continue to exist today. We then explore present day trends of neighbourhood commercial development. We discuss how to retain main street commerce (benefits, threats, and the role of Business Improvement Areas). We also investigate the challenge and success of growing new tower-based businesses in Toronto (looking to Regent Park and Thorncliffe Park as examples). Next, we look to the future of retail, exploring innovative business approaches from Toronto and elsewhere, including models such as shared spaces, pop-up retailing, mixed use light industrial, and small-space curation. Based on the insight from the literature, best practices, business performance, site observations, and interviews with key informants, we identify actionable recommendations for developing vibrant, inclusive, and sustainable commercial spaces in Toronto’s future neighbourhoods. These recommendations suggest how new neighbourhoods can “achieve new standards of sustainability, affordability, mobility, and economic opportunity” (Sidewalk Labs, 2018). We explore the past, present, and future of Toronto’s commercial spaces in order to glean transferable lessons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".