Une diversité accrue des suburbs, quelle conséquence politique ? L’exemple du Grand Toronto
Bibliographic record
Abstract
Les banlieues nord américaines, longtemps décrites comme des lieux riches et ethniquement peu divers, connaissent depuis deux décennies une lente mais profonde transformation dont les manifestations les plus vives sont la densification du bâti et la diversification croissante des populations résidentes. Toronto, capitale économique du Canada, s’est longtemps inscrite dans ce modèle urbain nord américain où la ville-centre s’opposait aux suburbs . Elle présentait une plus grande diversité dans la composition sociale et ethnique de sa population tandis que les banlieues de l ’outer rin g étaient caractérisées par une plus grande homogénéité et richesse. De nos jours, ces banlieues connaissent une mutation de leur profil sociologique dont un premier bilan politique est tiré ici.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".