Is poverty concentration expanding to the suburbs? Analyzing the intra-metropolitan poverty distribution and its change in Montreal, Toronto and Vancouver
Bibliographic record
Abstract
A spatial decentralization of the low-income population has been observed in many major \nU.S. metropolitan areas as well as in their Canadian counterparts. Few Canadian \nstudies have explored whether this change is a trend that extends across the suburban \nspace or if it is limited to specific areas of the metropolitan region. The goal of this article \nis to verify if suburban poverty in Montreal, Toronto and Vancouver is a phenomenon \nlimited to older suburbs or if it is increasing in more recent suburban areas as well. \nBy elaborating a typology of the metropolitan region, we examine if changes in poverty \nbetween 1986 and 2006 are associated with specific types of built environment. We \nthen evaluate if this association is maintained when we introduce additional explanatory \nfactors related to gentrification and neighbourhood decline. We explore if the geography \nof poverty in the main CMAs evolves in a similar pattern or if each CMA has its \nown particularities. Our results reveal that areas of rising poverty are mostly located in \npost-war suburbs. These areas share some of the characteristics of inner-city poor \nneighbourhoods and differ in some ways from the typical middle- and upper-class suburbs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".