Big Boxes versus Traditional Shopping Centers: Looking At Households' Shopping Trip Patterns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, the competition between, on the one hand, regional and super-regional shopping centers and, on the other hand, “category killers” and “big boxes” is analyzed using discrete choice modeling (logistic regression). An extensive Origin-Destination phone survey in the Quebec Metropolitan Area in 2001 provides detailed information on both households' socioeconomic and demographic profiles and daily trip patterns, making it is possible to identify and model customers' shopping destination choices. The findings suggest that several trip and household attributes impact customers' choice for either big boxes or traditional shopping centers: trip purpose, transportation mode and car ownership, day of the week, departure time and place as well as trip length and, finally, respondent's gender, age and type of household.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it