Dallas-Fort Worth et Toronto : des très grandes villes aux centres-villes malmenés (Dallas-For Worth and Toronto : two larges cities with threaten dowtowns)
Bibliographic record
Abstract
Résumé. - L'équipement commercial en Amérique du Nord est marqué par des surfaces moyennes de magasin plus grandes qu'en Europe, des prix plus faibles mais des distances à parcourir par le consommateur qui sont plus grandes. Au sein de l'appareil commercial, les chaînes et les nouvelles formes de commerces - magasins d'usine... - sont de plus en plus nombreuses, notamment dans les banlieues. Ceci désavantage le centre-ville car le foncier y est cher et relativement rare tandis que la circulation automobile y est malaisée. Néanmoins, l'examen de Toronto et de Dallas - Fort Worth révèle qu'au-delà de ces points communs, l'évolution n'est pas partout aussi radicale.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".