Food industry degrowth as a public health strategy: the case of ultra processed baked goods
Bibliographic record
Abstract
Evidence associates ultra-processed food and beverage (UPF) diets to diverse non-communicable diseases, including obesity, diabetes, cardiovascular disease and mental health disorders. Efforts to reformulate by reducing salt, sugar and unhealthy fats in such foods, have not changed the fact that UPFs are an increasing proportion of population diets. The UPF industry is rooted in growth – a fundamental logic at the heart of publicly traded and for-profit private corporations. Our longitudinal analysis of supermarket trade journals from the US and UK spanning 30 years finds that growth in this sector is not demand-led, but industry driven. Our analysis uncovers four dynamics of this growth paradigm, which we call combatition., swotification, the fashion spiral, and demand-pumping. Our findings show that, while public health policies result in individual products becoming more ‘healthy’, these benefits are likely to dwarfed by the aggregate (overall) growth of the UPF category itself. We propose a powerful counter paradigm: de-growth. While having gained recognition in ecological economics, we demonstrate its potential for public health, especially its concepts of decoupling and overproduction. While many public health interventions exist within a growth paradigm, a degrowth perspective proposes that the UPF industry cannot innovate its way out of health harms, and that the production of the entire category must decrease.
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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 | 0.005 | 0.006 |
| 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 | 0.013 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".