Are new patterns of low‐income distribution emerging in Canadian metropolitan areas?
Bibliographic record
Abstract
Recent studies on urban poverty in Canadian cities suggest a growing spatial concentration of poor populations within metropolitan regions. This article assesses trends in the intra‐urban distribution of the poor population from 1986 to 2006 in eight of Canada's largest cities. We consider five well‐known dimensions of segregation, as identified by Massey and Denton (1988), in order to examine changes in the spatial distribution of poor populations within metropolitan areas: evenness, exposure, concentration, clustering, and centralization. These indices were calculated for low‐income populations at the census tract level using data from five Canadian censuses. Although each metropolitan area has distinctive characteristics, we were able to identify some general trends. The results suggest that, in 2006 compared to 1986, low‐income populations lived in more spatially concentrated areas, which were, at the same time, socioeconomically more homogeneous and more dispersed throughout the metropolitan area. In addition, we observed that over the last twenty years areas of poverty have been located, for the most part, in neighbourhoods adjacent to downtown cores. Nevertheless, we found that poverty has mostly increased in suburban areas located outside inner‐city neighbourhoods. Growing socioeconomic homogeneity and dispersion of low income areas in metropolitan areas reveal new spatial patterns of urban poverty distribution. These findings should be cause for concern as social isolation in the most disadvantaged neighbourhoods could affect the life chances and opportunities for the residents of those areas .
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".