Pathways to suburban poverty in nine Canadian metropolitan areas
Bibliographic record
Abstract
The suburbanization of poverty has been a concerning trend in many urban regions. While research has described patterns of suburbanization of poverty at regional and neighbourhood levels, there are open questions about how lower-income households have agglomerated in the suburbs in recent history. Is suburban poverty primarily a result of 1) the movement of low-income residents from central to suburban neighbourhoods (e.g., via processes of gentrification and displacement), 2) migration between Census Metropolitan Areas and the immigration of low-income groups to suburbs, or 3) becoming and remaining poor while staying in the suburbs? The objective of this paper is to describe and quantify the propensity of these predominant individual geographic pathways to suburban poverty. We do so via a cluster analysis of census and land use data to define neighbourhoods as either central or suburban, and then link this categorization to a large-scale panel dataset representing 20 % of tax filers in Canada (from 2006 to 2016). These data allow for analyzing different pathways within the context of large Canadian metropolitan areas, specifically to what extent poverty in suburban neighbourhoods stems from intra-urban residential mobility, immigration, and becoming poor in-place. We find that becoming and remaining poor while staying in the suburbs encompasses a greater proportion of suburban poverty than immigration and centre-to-suburb residential mobility combined. Overall, this research expands our understanding of the sources of suburban poverty while also providing pertinent information to aid preventative policy aimed at reducing suburban poverty in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".