Changes to Food Service Within Super-Regional Shopping Centres in Toronto
Bibliographic record
Abstract
This research explores how the changes in food services have affected super-regional shopping centres in the Toronto Census Metropolitan Area between the years of 2014 and 2019. As shopping centres have begun to shift some of their operations away from traditional retailers and begun to invest in food services throughout the centre. The research examines seven food service categories and how each of them affect the overall trade areas of the shopping centre. Trade areas were created through a 60:40 weighting system for the attractiveness of each shopping centre, with 60% going to retail while 40% going to food services. The results of this study indicated that a growth in Fast-Casual, Gourmet Food, and Impulse food services across all shopping centres. While trade areas of shopping centres have seen mixed results due to the growth of food services. Keywords: Food Services, Shopping Centres, Huff Model, Toronto
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".