Feeding the green gentrification machine: urban agriculture and the barriers to a just ecological transition in Montréal, Québec
Bibliographic record
Abstract
As cities embrace urban greening, the impacts of sustainability initiatives are uneven, and research on progressive urban governments highlights barriers to meaningful structural change. This paper examines Montréal under the leadership of Projet Montréal, a party widely perceived as progressive and environmentally oriented, to analyze how urban agriculture can contribute to green gentrification. Drawing on interviews with planners, developers, and community actors, as well as discourse analysis of municipal policy documents, it traces how urban agriculture evolves from a grassroots practice to a sustainability fix institutionalized through negotiated planning. This paper makes two key contributions. First, a long-term perspective on a progressive municipal government shows how ecological initiatives are prioritized over housing through alignment with green growth coalitions. Second, the study demonstrates how sustainability fixes allow administrations to reconcile progressive rhetoric with growth imperatives, resulting in policy trade-offs that undermine housing affordability. These findings advance green gentrification research by revealing how progressive governments reproduce structural injustices through their ecological transition agendas within the context of a housing crisis. This paper offers policy insights on integrating greening with housing protections as a prerequisite for a just transition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".