Bibliographic record
Abstract
Explanations of urban segregation by income must look to the housing system, alongside local factors. In welfare capitalist countries, the respective roles of private and social rental cannot be assumed. This study draws on policy literature as well as census and administrative data to explain how a shifting housing policy and market regime shaped the evolving segregation of low-income renters in Greater Toronto, 1971-2006. A huge postwar private-rental apartment building sector, and greatly expanding social housing, almost equally shaped this geography. The postwar regime created a ‘mixed economy of rental’, mixed-income suburbs, and two decades of sustained central-city mix despite gentrification. Social housing at 10-12 percent of total housing production—departing from long-run trends in liberal-welfare Canada—absorbed half of low-income demand, with large mitigating impacts on neighbourhood change. After 1980, neoliberal trends of declining rental incomes and production directly fed more segregation, inner-suburban ‘decline’, and less-mixed outer suburbs. Spatial patterns arose from distinctive national and local influences as well as reflecting international trends. The study confirms the significance of metropolitan growth in small-area trends, and of dispersed rental production in spatial income mix. The findings have relevance internationally in contexts of mixed social and private rental systems and surging ‘built-to-let’ production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".