Aires de marché et choix des destinations de consommation pour les achats réalisés au cours de la semaine – Le cas de la région de Québec
Bibliographic record
Abstract
Since the early 1990s, big box stores have profoundly modified the retail structure of Canadian cities. This entails multiple consequences, beyond the mere competition between establishments, for the evolving relationships between diversity of retail forms, consumer behaviour and urban planning. These relationships are explored in the Québec Metropolitan Community, using detailed databases, including establishment directories, mobility surveys and a virtual road network, integrated into a regional GIS. Trade areas of commercial streets, shopping centers and big box stores are delineated and analyzed. Consumers' attributes, such as age, gender, type of household, mode of transport, which putatively influence the probability of patronizing one type of retail cluster compared to another type, are modelled using logistic regression. The influence of the relative location of cluster types on their capacity to attract consumers is also modelled in order to gain some understanding of the competition among and between types. The analysis suggests that, by and large, the growing number of big box stores has more negative consequences for shopping centres than they have for commercial streets. The study also clearly reveals the growing importance of shopping trips in the mobility profile of households and provides a knowledge base useful for urban planning.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".