Advancing the research agenda on food systems governance and transformation
Bibliographic record
Abstract
The food systems upon which humanity depends face multiple interdependent environmental, social and economic threats in the 21st Century. Yet, the governance of these systems, which determines to a large extent the ability to adapt and transform in response to these challenges, is underresearched. This perspective piece synthesises the findings of two recent reviews of food systems governance and transformations and proposes a comprehensive research agenda for the coming years. These reviews highlight the influence of governance on food systems, methodological obstacles to explaining the effectiveness of governance in realising food sustainability, and conditions that have historically supported food system transformations. We argue that the following steps are key to improving our knowledge of the role of governance in food systems: (1) developing more comparable research designs for building generalisable explanations of the governance elements that are most effective in realising food systems goals; (2) using the lens of polycentricity to help disentangle complex governance networks; (3) giving greater attention to the conditions and pre-conditions associated with historical food system transformations; (4) identifying adaptations that strengthen or weaken path dependency; and, (5) focusing research on how transformations can be supported by institutions that facilitate collective action and stakeholder agency.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.013 | 0.034 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".