Civil society engagement in food systems governance in Canada
Bibliographic record
Abstract
Civil society organizations (CSOs) commonly experience food systems governance as imposed by governments from the top down and as unduly influenced by a small group of private sector actors that hold disproportionate power. This uneven influence significantly impacts the activities and relationships that determine the nature and orientation of food systems. In contrast, some CSOs have sought to establish participatory governance structures that are more democratic, accessible, collaborative, and rooted in social and environmental justice. Our research seeks to better understand the experiences of CSOs across the food systems governance landscape and critically analyze the successes, challenges, and future opportunities for establishing collaborative governance processes with the goal of building healthier, sustainable, and more equitable food systems. This paper presents findings from a survey of CSOs in Canada to identify who is involved in this work, key policy priorities, and opportunities and limitations experienced. Following the survey, we conducted interviews with a broad cross-section of CSO representatives to deepen our understanding of experiences engaging with food systems governance. Our findings suggest that what food systems governance is, how it is experienced, and what more participatory structures might look like are part of an emergent and contested debate. We argue for increased scholarly attention to the ways that proponents of place-based initiatives engage in participatory approaches to food systems governance, examining both current and future possibilities. We conclude by identifying five key gaps in food systems governance that require additional focus and study: (1) Describing the myriad meanings of participatory food systems governance; (2) Learning from food movement histories; (3) Deepening meaningful Indigenous-settler relationships; (4) Addressing food systems labor issues; and (5) Considering participatory food systems governance in the context of COVID-19.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.045 | 0.017 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".