The Evolution of City-Region Food Governance in Montreal \nFood Politics, Policy and Planning Under Quebec’s Neoliberal Turn
Bibliographic record
Abstract
Municipal Food policy councils (FPC) are emerging across Canada. They are innovative governance models that fill an institutional gap by activating policy and engagement at the municipal level. Territorial, or city-region, food systems are the systems of innovation, which combine the goals of quality, health, ecology, fairness and participative democracy. Multi-level and cross-sectorial partnerships can help re-adjust the institutional context to enable, facilitate and champion the emergence of social innovations carried by civic food networks (CFNs). The process of creating an FPC opens a window on the food politics of a place, its actors, and history. This thesis illustrates some of the challenges city-regions may face by providing an in-depth case study of Montreal, Quebec. This thesis explores the influence of the provincial “neoliberal turn” on food planners and CFNs since early 2000. I highlight how neoliberalism has interacted with the institutional legacy of the "Quebec model". In turn, I follow the social formation of groups and coalitions that shape the regional policy networks from 1986 to 2016 and their interactions with public sector organizations. This work adopts a multi-method approach to analyze the negotiations and arrangements within the 2014-2016 Montreal Food Systems Action Plan. Specifically, this thesis uses an assessment tool built on the premises of Actor-Network Theory to evaluate whether the action plan meets the conditions for an effective partnership. I identify the territorial, technical and political dynamic inside and outside this hybrid public sector-civil society partnership to explain its eventual transition into a municipally mandated Food policy council.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".