Citizen-led Urban Agriculture and the Politics of Spatial Reappropriation in Montreal, Quebec
Bibliographic record
Abstract
Urban Agriculture (UA) has been practiced in Montreal, Quebec for well over a century. In the last five years or so, a renewed enthusiasm for UA has manifested itself in the form of citizen-led UA projects. The latter are often established in residual spaces, from vacant lots to sidewalks, and alleyways. These more spontaneous and informal UA practices point to a shift in how urban inhabitants perceive and use urban space. Through a case study of informal UA projects in Montreal, QC, this work brings attention to the dynamics surrounding the establishment of citizen-led UA projects, paying special attention to their complex structure. Indeed, although they are usually initiated by groups of citizens, other actors are either directly or indirectly involved, including non-profit organizations, municipal officials, or business owners. To better understand these processes, I ask the following questions: Why are citizens in Montreal reappropriating vacant and underused urban spaces for UA? How are these spaces being established, and who is involved? How might these spaces and the social relations forged within them, contribute--or not--to a democratic urban politics? Bringing together existing scholarship on critical urban agriculture, radical democracy, and urban geography, this research exposes some of the inherent tensions present in contemporary UA. This work demonstrates that collective UA projects exist simultaneously as a political practice, and one that might not significantly alter the existing spatial and social orders.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".