La concurrence entre les centres commerciaux. Une analyse du point de vue du consommateur
Bibliographic record
Abstract
Résumé Les récents changements dans l'industrie des centres commerciaux accroissent l'intérêt des études consacrées au processus de choix de ces formes de vente. Dans la présente étude, nous montrons que ce processus de décision est séquentiel. De fait, il y a une asymétrie concur-rentielle entre les centres commerciaux. Les acheteurs choisissent en premier lieu un type de centre, et ensuite un choix concret dans ce type. Ce choix est conditionné par quatre variables, à savoir: l'image du centre, le coût de déplacement pour s'y rendre, le facteur premiére visite et, enfin, la concurrence intertype. Cette dernière variable analyse l'impact de la concurrence entre les centres commerciaux et les autres types de commerce sur la concurrence entre les centres. Abstract Recent developments in the shopping mall industry have increased the interest in studies that account for consumer's choice process. This paper shows that such a choice process is sequential. As a matter of fact, there is competitive asymmetry between shopping malls; consumers first choose a shopping mall type and then a specific shopping mall belonging to this type. According to our study, the choice depends on four variables: shopping mall image, travel cost, the “first visit” factor, and “intertype competition”, which relate to the impact that competition between shopping malls and other kinds of commerce can have on competition between shopping malls.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".