Bibliographic record
Abstract
This paper examines the trends and changes in both spatial and non-spatial income inequality in the Toronto CMA between 1985 and 2015 at various geographic scales, including both within and between neighbourhoods. Fixed effects panel regression models are used to uncover which local demographic and housing characteristics are most significant in explaining changes in inequality within neighbourhoods over time. Findings indicate that macro-scale income segregation among neighbourhoods has declined, while micro-scale intra-neighbourhood income segregation has increased since 1985. Further, compared to overall changes in income inequality in the region, neighbourhoods have become more homogenous in terms of their household income distribution. Thus, neighbourhood sorting by households based on income has increased since 1985. Consistent with extant literature, local housing characteristics have spillover effects on income segregation. Specifically, variables associated with greater housing affluence are negatively correlated with intra-neighbourhood inequality measures, and thus positively correlated with income homogenization. This confirms and adds to the literature that local land use regulations impact income spatial inequality. KEYWORDS Spatial Income Inequality; Segregation; Neighbourhoods; Toronto CMA; Fixed Effects Models; Quantitative Analysis; GIS; Housing Regulation
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".