Challenging agricultural norms and diversifying actors: Building transformative public policy for equitable food systems
Bibliographic record
Abstract
Food systems governance regimes have long been spaces of “thick legitimacy” (Montenegro de Wit & Iles, 2016), where embedded norms benefit productivist agricultural practices. Within governance regimes, the science-policy interface and the scientists who occupy this space are integral in today’s public policy processes. Often treated as objective science, technical disciplines have become a powerful source of legitimatizing in decision making. Without the contextualization of lived experience or diverse ways of knowing, these siloed spaces can lead policymakers towards an action bias (e.g., a rush to short-term solutions) that neglects the underlying causes and concerns of our current crises. Current governance arrangements in the science-policy interface demonstrate the bias toward technical science (e.g. economics) and short-term solutions. However, by challenging productivist agriculture norms reformed public policy processes may shift from a space of repression to one of possibility. This reform can happen through investigatiing dominant actor coalitions and identifying tools to reconfigure these power arrangements. Public policy theory, such as the advocacy coalition framework (ACF), helps organize relations within current agricultural policy arenas. The work of practitioners and other disciplines offer tools that can support transformative action by food systems advocates in the pursuit of changing the way public policy is made. In part, understanding how power is organized and who may influence policy processes is critical to change. This reflective essay ends with tools and strategies for those wishing to engage governments in this shift. The proposed tools and strategies focus on how people (e.g. policy champions), processes (e.g. policy leverage points), and partnerships (e.g. allyship) generate ways in which advocates can, and do, engage governments in transformative 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 | 0.033 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.088 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".