Fostering the Catalyst Role of Government in Advancing Healthy Food Environments
Bibliographic record
Abstract
Effective approaches to non-communicable disease (NCD) prevention require intersectoral action targeting health and engaging government, industry, and society. There is an ongoing vigorous exploration of the most effective and appropriate role of government in intersectoral partnerships. This debate is particularly pronounced with regards to the role of government in controlling unhealthy foods and promoting healthy food environments. Given that food environments are a key determinant of health, and the commercial sector is a key player in shaping such environments (eg, restaurants, grocery stores), the relationship between government and the commercial sector is of primary relevance. The principal controversy at the heart of this relationship pertains to the potential influence of commercial enterprises on public institutions. We propose that a clear distinction between the regulatory and catalyst roles of government is necessary when considering the nature of the relationship between government and the commercial food sector. We introduce a typology of three catalyst roles for government to foster healthy food environments with the commercial sector and suggest that a richer understanding of the contrasting roles of government is needed when considering approaches NCD prevention via healthy food environments.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".