The case for developing a cohesive systems approach to research across unhealthy commodity industries
Bibliographic record
Abstract
OBJECTIVES: Most non-communicable diseases are preventable and largely driven by the consumption of harmful products, such as tobacco, alcohol, gambling and ultra-processed food and drink products, collectively termed unhealthy commodities. This paper explores the links between unhealthy commodity industries (UCIs), analyses the extent of alignment across their corporate political strategies, and proposes a cohesive systems approach to research across UCIs. METHODS: We held an expert consultation on analysing the involvement of UCIs in public health policy, conducted an analysis of business links across UCIs, and employed taxonomies of corporate political activity to collate, compare and illustrate strategies employed by the alcohol, ultra-processed food and drink products, tobacco and gambling industries. RESULTS: There are clear commonalities across UCIs' strategies in shaping evidence, employing narratives and framing techniques, constituency building and policy substitution. There is also consistent evidence of business links between UCIs, as well as complex relationships with government agencies, often allowing UCIs to engage in policy-making forums. This knowledge indicates that the role of all UCIs in public health policy would benefit from a common approach to analysis. This enables the development of a theoretical framework for understanding how UCIs influence the policy process. It highlights the need for a deeper and broader understanding of conflicts of interests and how to avoid them; and a broader conception of what constitutes strong evidence generated by a wider range of research types. CONCLUSION: UCIs employ shared strategies to shape public health policy, protecting business interests, and thereby contributing to the perpetuation of non-communicable diseases. A cohesive systems approach to research across UCIs is required to deepen shared understanding of this complex and interconnected area and also to inform a more effective and coherent response.
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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 | 0.361 | 0.211 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.025 | 0.014 |
| Science and technology studies | 0.017 | 0.136 |
| Scholarly communication | 0.047 | 0.060 |
| Open science | 0.012 | 0.040 |
| Research integrity | 0.017 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 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".