Between state–community partnerships and austerity: The everyday networked governance of Montreal’s solidarity greenhouses
Bibliographic record
Abstract
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 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 | 0.003 | 0.004 |
| 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 | 0.019 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".