Exploring the Relationship between Big-Box Retail and Consumer Travel Demand in the Greater Toronto Area
Bibliographic record
Abstract
Canada’s retail landscape has been structurally transformed by the widespread development of large format (big-box) retail since the mid-1990s. Emphasis placed on convenience, price, and auto-based accessibility, coupled with design elements of big-box agglomerations has produced new modes of consumer retail interaction. In view of these recent changes, it is surprising that little effort has been extended to studying the transportation impacts of big-box retail. This paper explores the relationship between consumer travel behaviour and the expansion of large format retail facilities within Canada’s largest metropolitan region, the Greater Toronto Area (GTA). Data have been drawn from the 1996 and 2001 Transportation Tomorrow Surveys (TTS) and combined with a longitudinal retail structural database. Regional travel flows and “big-box” case studies suggest considerable auto-dependence for shopping activities, particularly in the suburban cities of the GTA. Rising retail capacity at case study locations appears to have been matched by a dramatic increase in auto-based shopping travel. Evidence from this research points to a potential gap between consumer activities and the prevailing sustainability objectives of transport and land use policy initiatives.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".