Gentrification, Social Mix, and Social Polarization: Testing the Linkages in Large Canadian Cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| 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 it