Policy insights from the nutritional food market transformation model: the case of obesity prevention
Bibliographic record
Abstract
This paper presents a system dynamics policy model of nutritional food market transformation, tracing over-time interactions between the nutritional quality of supply, consumer food choice, population health, and governmental policy. Applied to the Canadian context and with body mass index as the primary outcome, we examine policy portfolios for obesity prevention, including (1) industry self-regulation efforts, (2) health- and nutrition-sensitive governmental policy, and (3) efforts to foster health- and nutrition-sensitive innovation. This work provides novel theoretical and practical insights on drivers of nutritional market transformations, highlighting the importance of integrative policy portfolios to simultaneously shift food demand and supply for successful and self-sustaining nutrition and health sensitivity. We discuss model extensions for deeper and more comprehensive linkages of nutritional food market transformation with supply, demand, and policy in agrifood and health/health care. These aim toward system design and policy that can proactively, and with greater impact, scale, and resilience, address single as well as double malnutrition in varying country settings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".