Cultivating Montreal: A Brief History of Citizens and Institutions Integrating Urban Agriculture in the City
Bibliographic record
Abstract
Can concerted citizen action, involvement of community groups and institutions, as well as formal authorities, play important enough roles in promoting urban agriculture? The case of Montreal indicates they can. This paper investigates some of these interventions at different times and levels and explores how they made this North American city a leader in that field. Urban agriculture activities began in the early 1970s, but gained momentum after the 1973 oil crisis. The role of diverse players in transforming the city fabric ever since is discussed here by introducing and developing related projects in three parts: the first traces the history of community gardens; the second presents Montreal‐based pilot projects rooted in different neighborhoods that aimed to intertwine urban agriculture, design and citizens that the authors developed and implemented; the third discusses a recent (2012) citizens’ action that used a municipal bylaw to hold a public consultation on the state of urban agriculture and towards the formation of city's Comité de travail de la collectivité montréalaise en agriculture urbaine or Permanent Committee on Urban Agriculture.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".