The Evolution of Commercial Structure in the North American City: A Toronto Case Study
Bibliographic record
Abstract
This paper generalizes the evolution of commercial structure in the Greater Toronto Area, based \non a series of papers and field surveys carried out by the Centre for the Study of Commercial \nActivity at Ryerson University. It identifies four stages in the sequence: Traditional, Planned \nShopping Centres, Power Retail, and (arguably) E-commerce; and it identifies the time period \nand spatial footprint for each stage of development. The main contribution of the paper, \nhowever, is the exploration of the business model for each one, and the way each model \nprovides an opportunity for the emergence of the subsequent structure. \nAlthough each retail stage dominates a particular period of time and the portion of the urban \narea under development at that time, the retail fabric from earlier periods also remains in place, \nand today’s retailers and consumers can select the kinds of facilities that are appropriate to their \nlifestyle. At the same time it is possible to observe how the pressures for greater economic \nefficiency (scale economies, standardized formats) compete with the consumer's desire for \nvariety, degree of service and ease of access. We are moving towards a world where each \nhousehold can shop in a variety of ways.
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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 | 0.002 | 0.007 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".