Green alleys in Montreal: tensions between gentrification and environmental justice
Bibliographic record
Abstract
Montreal’s Green Alley Program (GAP), in place since 1995, enables the conversion of back alleys into green and community spaces. The program relies on a participatory approach to planning: in most boroughs, the creation of green alleys depends on residents’ demands. Residents are extensively involved in the implementation and maintenance of the alleys; therefore, the selection of the projects and of the applicants is key for the long-term durability of the program. Green alley workers tend to identify “good” applicants that closely align with the typical profile of gentrifiers, implying that some assumptions guiding the selection process may indirectly impact the implementation of the program and its outcomes. Using interviews done with green alley workers, this paper analyzes the GAP through the lens of environmental gentrification, exploring the idea of a potential relationship between neighborhood change and the increase in the number and quality of green alleys. The paper focuses on green alley workers’ reflexivity on the program, showing that they are conscious of the tensions that are constitutive of the relationship between gentrification and green alleys, but are working within structural constraints, thereby contributing to the reproduction and reinforcement of socio-spatial inequalities and patterns of environmental injustice.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".