Democratizing food systems: A scoping review of deliberative mini-publics in the context of food policy
Bibliographic record
Abstract
Deliberative mini-publics (DMPs) have attracted growing attention from both researchers and practitioners in recent years. Their purpose is to assemble random groups of citizens, representing a cross section of society, in order to engage in discussions about policy issues and formulate recommendations. During these sessions, participants are exposed to contrasting perspectives from experts and engage in respectful internal deliberations, facilitated by organizers, before arriving at a carefully considered joint policy position on the topic at hand. DMPs are grounded in the belief that citizen involvement and input are essential if policy reforms are to be perceived as legitimate by the public. In the agri-food domain, they represent an innovative way to rebuild public trust in the food system, allowing citizens to reshape food policy in alignment with their values and concerns. In this study, we conducted a scoping review of the literature to assess the contexts in which food-related DMPs emerge, as well as their organizational characteristics, procedural qualities, and results. We identified a total of 24 case studies, revealing significant diversity between DMPs in terms of their policy themes, formats, and recruitment and decision-making procedures. In terms of results, participants reported that attending the DMP had been a positive experience and had increased their awareness of, and ability to engage in, food policy debates. However, only a handful of DMPs led to documented policy reforms. We argue that greater emphasis should be placed on post-deliberation activities and dialogues if DMPs are to make a meaningful impact and contribute to the democratization of food systems.
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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 | 0.065 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.028 | 0.033 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".