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Record W4417295597 · doi:10.1177/00420980251398620

Between state–community partnerships and austerity: The everyday networked governance of Montreal’s solidarity greenhouses

2025· article· en· W4417295597 on OpenAlexafffundabout
Nathan McClintock, Sophie L. Van Neste, Chantal Gailloux, Florence Barnabé, Caroline Flory-Célini

Bibliographic record

VenueUrban Studies · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsInstitut National de la Recherche Scientifique
FundersInstitut national de la recherche scientifique
KeywordsAusterityCorporate governanceSolidarityPopularityEveryday lifeFunction (biology)Collaborative governancePower (physics)

Abstract

fetched live from OpenAlex

Greenhouses are becoming a regular feature of the urban landscape, their popularity driven in part by an eco-futurist, techno-optimist vision of “vertical farming” that articulates with entrepreneurial green urbanism. In Montreal (Quebec, Canada), however, urban greenhouses tend to be small-scale, low-tech infrastructures operated by networks of state, non-profit, and community actors, with an equity-oriented mission of working in solidarity with–and providing material support for–marginalized populations. In this article, we characterize the everyday governance of these “solidarity greenhouses” ( serres solidaires ) and examine the conditions that mediate their emergence and success within twin contexts of austerity and entrepreneurial urbanism. Examining three key challenge areas–project definition, municipal regulations, and funding–and how project leaders navigate them, we reveal how everyday governance is a function of relational and differential power between partners and the ability to navigate a shifting funding landscape. Governance is further deeply influenced by Quebec’s unique “community action” model and the ongoing dismantling of a once-robust welfare state.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.061
GPT teacher head0.264
Teacher spread0.204 · 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 designObservational
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

Citations0
Published2025
Admission routes3
Has abstractyes

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