Advancing food systems governance: Perspectives of Canadian civil society organizations
Bibliographic record
Abstract
Civil society organizations (CSO) engaged in food systems work have grown substantially in number, scale, and scope. Many of these CSOs are seizing opportunities to engage with the globalized industrial food system in efforts to promote greater equity and sustainability and have realized that addressing issues at a systemic level demand engaging with governance. Despite increasing scholarly attention to food systems and to CSO engagement with governments, the diversity of governance arrangements remains understudied. In this paper, we explore the meanings and perspectives of food systems governance from the standpoint of CSOs leaders across Canada and Indigenous territories. Drawing on 70 semi-structured interviews, we argue that CSOs play a central role in advancing food systems governance by how they frame and act on food issues. We point to CSOs understandings and engagement with food systems governance as broader and more nuanced than what has previously been documented in the literature. A more discerning and comprehensive understanding of how CSO actors describe and advance food systems governance helps articulate how CSOs are scaling-up place-based work, modelling new forms of governance, and ultimately, impacting decision-making structures.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.052 | 0.032 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".