Local state policy and ‘new‐build gentrification’ in Montréal: the role of the ‘population factor’ in a fragmented governance context
Bibliographic record
Abstract
Abstract This paper contributes to the debate on the role of public policy in gentrification through a case study of three phases of proactive promotion of residential intensification, especially to increase homeownership in central neighbourhoods in the City of Montréal (Québec, Canada). This case is used to explore the following questions: Where municipalities seek to enact ‘urban revitalisation’ by using housing policy tools to manipulate the size and composition of the resident population, what are the influences of local/regional fiscal and jurisdictional contexts? How are these articulated with the city's economic development trajectory? How do neoliberal policy turns emanating from scales beyond municipal jurisdiction influence the balance worked out in the domain of local housing policy between, on the one hand, local states' preoccupation with ‘fiscal pragmatism’ and local investment climates, and on the other hand, the demands they face to use their powers to reduce social inequalities within their territory? In the urban geographical literature, municipal involvement in new‐build gentrification is seen as an expression of a broader turn to urban neoliberalism and the concomitant entrepreneurial turn by the local state. The paper shows that exploring the influence of discourses around the ‘population question’ can add depth, nuance and complexity to such analyses. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".