Exploring the global and local patterns of income segregation in Toronto, Canada: A multilevel multigroup modeling approach
Bibliographic record
Abstract
Residential income segregation is a spatial manifestation of social inequality and is an important factor that influences access to resources, services, and amenities. In general, past research analyzing income segregation has applied index-based methods to describe the separation of low-income households at one spatial scale; however, existing studies have not yet considered how income segregation varies across multiple income classes, spatial scales, and local contexts. This study applies a multilevel multigroup modeling approach to explore the global and local patterns of income segregation between dissemination areas (micro-scale), census tracts (meso-scale), and neighborhoods (macro-scale) in Toronto, Canada. A global model that estimates the overall multiscale segregation of five income classes finds that the most affluent families had the highest levels of segregation and that the segregation of all income classes was strongest at the macro- and micro-scales. A local model that allows the micro-scale segregation measures to vary geographically shows that higher-income families were less segregated in the city center than in the inner suburbs, that middle-income families were highly segregated in areas serviced by public transit, and that almost all income classes had high levels of segregation in disadvantaged neighborhoods prioritized for investment by local policymakers. The methodological and substantive contributions of this study for understanding the complex patterns of income segregation are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".