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Record W2517889948 · doi:10.2134/urbanag2015.01.1511

Cultivating Montreal: A Brief History of Citizens and Institutions Integrating Urban Agriculture in the City

2016· article· en· W2517889948 on OpenAlexaffabout
Vikram Bhatt, Leila Marie Farah

Bibliographic record

VenueUrban Agriculture & Regional Food Systems · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsToronto Metropolitan UniversityMcGill University
Fundersnot available
KeywordsUrban agricultureAgriculturePublic administrationPolitical scienceState (computer science)Action (physics)Economic growthEnvironmental planningGeographyArchaeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.208
Teacher spread0.172 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2016
Admission routes2
Has abstractyes

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