Gentrification, Social Mix, and the Immigrant-Reception Function of Inner-City Neighbourhoods: An Updated Analysis, 1971 - 2006
Bibliographic record
Abstract
Gentrification in the form of ‘neighbourhood revitalization’ is increasingly touted as one way of decreasing the social exclusion of residents of poor inner-city neighbourhoods 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 the inner-city retains its immigrant-reception function, whether gentrifying neighbourhoods can remain socially mixed, and whether neighbourhood compositional changes result in more or less of a polarized class and ethnic structure. This paper updates and expands upon research conducted by the author examining the effects of gentrification on levels of social mix and social polarization in Canadian inner cities. This research suggested that gentrification was generally associated with declining levels of ethnic and income mix, and with the loss of the immigrant-reception function in inner-city neighbourhoods, over the period 1971 to 2001 in Toronto, Montreal and Vancouver. The current paper updates this research using the just-published 2006 census data. As well, it expands the methodology to consider the effect of gentrification on levels of spatial polarization across the inner cities. The results demonstrate that the trends identified earlier linking gentrification to declining social mix have continued and magnified. Furthermore, the new analysis suggests that gentrification has produced increasing levels of neighbourhood income segregation and polarization across the inner cities of large Canadian metropolitan areas.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".