North American Gentrification? Revanchist and Emancipatory Perspectives Explored
Bibliographic record
Abstract
By offering a response to recent calls for a ‘geography of gentrification’, the author attempts to move on from the intractable theoretical divisions and overgeneralizations that continue to pervade the gentrification literature. The research described in this paper takes the form of a comparative assessment of the gentrifying neighbourhoods of South Parkdale, Toronto, Canada, and Lower Park Slope, New York City, USA. A central part of this research has been an engagement with two contrasting academic discourses on gentrification, the ‘emancipatory city’ (a Canadian construct) and the ‘revanchist city’ (a US construct), to examine how gentrification may or may not have changed since these discourses were produced and articulated. The author combines narratives from in-depth interviews (with a particular focus on displaced tenants) with supplementary data from secondary sources and demonstrates that gentrification is neither emancipatory nor revanchist in either case. This has important implications for how gentrification is understood and evaluated in Canada and the USA. Although one can see crucial broad similarities both in the causes and in the effects of gentrification in each neighbourhood (which would appear to endorse casual references to ‘North American gentrification’), the process is also differentiated according to contextual factors, and the nuances of the gentrification process are illuminated and clarified by international comparison. In sum, the author points to the need to exercise caution in referring to ‘North American gentrification’, especially as a geography of gentrification is only in its infancy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.049 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".