How Neoliberalism Is Shaping the Supply of Unhealthy Commodities and What This Means for NCD Prevention
Bibliographic record
Abstract
Alcohol, tobacco, and unhealthy foods contribute greatly to the global burden of non-communicable disease (NCD). Member states of the World Health Organization (WHO) have recognized the critical need to address these three key risk factors through global action plans and policy recommendations. The 2013-2020 WHO action plan identifies the need to engage economic, agricultural and other relevant sectors to establish comprehensive and coherent policy. To date one of the biggest barriers to action is not so much identifying affective policies, but rather how a comprehensive policy approach to NCD prevention can be established across sectors. Much of the research on policy incoherence across sectors has focused on exposing the strategies used by commercial interests to shape public policy in their favor. Although the influence of commercial interests on government decisions remains an important issue for policy coherence, we argue, that the dominant neoliberal policy paradigm continues to enable the ability of these interests to influence public policy. In this paper, we examine how this dominant paradigm and the way it has been enshrined in institutional mechanisms has given rise to existing systems of governance of product environments, and how these systems create structural barriers to the introduction of meaningful policy action to prevent NCDs by fostering healthy product environments. Work to establish policy coherence across sectors, particularly to ensure a healthy product environment, will require systematic engagement with the assumptions that continue to structure institutions that perpetuate unhealthy product 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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| 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".