Ultra‐processed products are becoming dominant in the global food system
Bibliographic record
Abstract
The relationship between the global food system and the worldwide rapid increase of obesity and related diseases is not yet well understood. A reason is that the full impact of industrialized food processing on dietary patterns, including the environments of eating and drinking, remains overlooked and underestimated. Many forms of food processing are beneficial. But what is identified and defined here as ultra-processing, a type of process that has become increasingly dominant, at first in high-income countries, and now in middle-income countries, creates attractive, hyper-palatable, cheap, ready-to-consume food products that are characteristically energy-dense, fatty, sugary or salty and generally obesogenic. In this study, the scale of change in purchase and sales of ultra-processed products is examined and the context and implications are discussed. Data come from 79 high- and middle-income countries, with special attention to Canada and Brazil. Results show that ultra-processed products dominate the food supplies of high-income countries, and that their consumption is now rapidly increasing in middle-income countries. It is proposed here that the main driving force now shaping the global food system is transnational food manufacturing, retailing and fast food service corporations whose businesses are based on very profitable, heavily promoted ultra-processed products, many in snack form.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".