The Right to Stay Put, Revisited: Gentrification and Resistance to Displacement in New York City
Bibliographic record
Abstract
Displacement has been at the centre of heated analytical and political debates over gentrification and urban change for almost 40 years. A new generation of quantitative research has provided new evidence of the limited (and sometimes counter-intuitive) extent of displacement, supporting broader theoretical and political arguments favouring mixed-income redevelopment and other forms of gentrification. This paper offers a critical challenge to this interpretation, drawing on evidence from a mixed-methods study of gentrification and displacement in New York City. Quantitative analysis of the New York City Housing and Vacancy Survey indicates that displacement is a limited yet crucial indicator of the deepening class polarisation of urban housing markets; moreover, the main buffers against gentrification-induced displacement of the poor (public housing and rent regulation) are precisely those kinds of market interventions that are being challenged by advocates of gentrification and dismantled by policy-makers. Qualitative analysis based on interviews with community organisers and residents documents the continued political salience of displacement and reveals an increasingly sophisticated and creative array of methods used to resist displacement in a policy climate emphasising selective deregulation and market-oriented social policy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".