Dimensions and dynamics of residential segregation by income in urban Canada, 1991–1996
Bibliographic record
Abstract
This paper examines the trends in residential segregation by income (post‐transfer, pretax income) in the thirty‐nine largest Canadian urban areas between 1991 and 1996. The study is motivated by the relative lack of attention paid to residential segregation by income in the Canadian context and by conceptual arguments linking compromised life chances and increased social tensions for the populations of highly segregated cities. We investigated several dimensions of segregation using five different measures (we focus on three of these here given the correlation structure of the measures) to examine changes in segregation between 1991 and 1996, a period characterised by economic recession, cutbacks in social programs and a widening of inequality in market incomes at the national scale. Overall, income segregation increased in most urban areas across all dimensions of segregation during the time period, with particularly high degrees of segregation observed in prairie cities (Winnipeg, Saskatoon and Regina). Of the three largest metropolitan areas (Vancouver, Toronto and Montréal), Montréal was the most consistently segregated. We also find that increases in spatial separation and spatial concentration by income occurred despite the fact that at the national scale, the tax and transfer system appeared to be adequately redressing a rise in inequality in labour and market incomes (as demonstrated by the lack of change in post‐transfer national income inequality measures during a period when inequality in market and labour incomes rose sharply). This implies that the lived experience of changes in income distribution are unlikely fully captured by aspatial, national‐scale measures and that intra‐urban measures with a spatial dimension are an important indicators of inequality in Canadian society.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".