Gentrification, Social Mix, and Social Polarization: Testing the Linkages in Large Canadian Cities
Bibliographic record
Abstract
Gentrification in the form of "neighborhood revitalization" is increasingly touted as one way of decreasing the social exclusion of residents of poor inner-city neighborhoods and of increasing levels of social mix and social interaction between different classes and ethnic groups. Yet the gentrification literature also suggests that the process may lead to increased social conflict, displacement of poorer residents to lower quality housing elsewhere, and, ultimately, social polarization. Much of this hinges on whether gentrifying neighborhoods can remain socially mixed, and whether neighborhood compositional changes result in more or less of a polarized class and ethnic structure. However, the impact of revitalization and gentrification on levels of social mix, income polarization, or ethnic diversity within neighborhoods remains unclear and under-explored. This study addresses this gap by examining the relationship between the timing of gentrification, changes in the income structure, and shifts in immigrant concentration and ethnic diversity, using census tract data for each decade from 1971 to 2001 in Toronto, Montreal, and Vancouver. This research demonstrates that gentrification is followed by declining, rather than improving, levels of social mix, ethnic diversity, and immigrant concentration within affected neighborhoods. At the same time, gentrification is implicated in the growth of neighborhood income polarization and inequality.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".