Bibliographic record
Abstract
Over the past decade, numerous studies have documented the successes and limitations of place-based initiatives that aim to promote healthy, equitable, and sustainable food systems. This research has also investigated the ways that these initiatives have become part of broad-based networks that connect a range of actors across sector, scale, and place. However, little attention has been given to how these different initiatives and networks engage with the state in an effort to impact food policy while adhering to goals of health, equity, and sustainability. In response, this chapter explores the intersections between food systems governance and social movement mobilization, examining the role of policy-making process and the efforts of non-profit organizations and grassroots coalitions to promote empowerment, community development, and broader food systems transformation. Specifically, we ask how social movements can advance food policy, while also modelling alternative food futures through processes of policy development. We pay particular attention to the complex ways in which these aims coexist, teasing out the tensions, possibilities, and the overall complexity of their interactions. Reflecting on a series of engagements in Canada, we argue that by prefiguring collaborative processes of engagement, critical inquiry, action, and reflexivity, social movement networks have the ability to strengthen relationships, analysis, and collective strategies for change.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".