Market pathways to food systems transformation toward healthy and equitable diets through convergent innovation
Bibliographic record
Abstract
Achieving food system transformation requires a deep understanding of the market mechanisms that underpin both the social benefits and the externalities of modern development. We examine how market dynamics affect the production and consumption of healthy and equitable diets in North America. Using causal loop diagramming, we show how three market feedback processes (industry capabilities, consumer category considerations, and systems and institutions) both constrain and enable food system transformation. Through behavioral-dynamic computational modeling, we demonstrate the ineffectiveness of isolated social or commercial interventions to achieve equitable access to nutritious foods across populations of varying socioeconomic statuses. Rather, self-sustaining transformations at scale require convergent innovations that bridge individual and collective action across typically siloed sectors, to achieve alignment between commercial, social, and environmental goals and activities. We discuss how this simulation-based analytical framework can inform policy for food system transformation, whether at the local, national, or global level. This paper explores how feedbacks involving social and material market infrastructure can both constrain and enable transformations towards more equitable, healthier food systems. It presents a comprehensive approach to guiding food system change, with a critical role for both individual and collective action across sectors.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".