The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing
Bibliographic record
Abstract
Given evident multiple threats to food systems and supplies, food security, human health and welfare, the living and physical world and the biosphere, the years 2016-2025 are now designated by the UN as the Decade of Nutrition, in support of the UN Sustainable Development Goals. For these initiatives to succeed, it is necessary to know which foods contribute to health and well-being, and which are unhealthy. The present commentary outlines the NOVA system of food classification based on the nature, extent and purpose of food processing. Evidence that NOVA effectively addresses the quality of diets and their impact on all forms of malnutrition, and also the sustainability of food systems, has now accumulated in a number of countries, as shown here. A singular feature of NOVA is its identification of ultra-processed food and drink products. These are not modified foods, but formulations mostly of cheap industrial sources of dietary energy and nutrients plus additives, using a series of processes (hence 'ultra-processed'). All together, they are energy-dense, high in unhealthy types of fat, refined starches, free sugars and salt, and poor sources of protein, dietary fibre and micronutrients. Ultra-processed products are made to be hyper-palatable and attractive, with long shelf-life, and able to be consumed anywhere, any time. Their formulation, presentation and marketing often promote overconsumption. Studies based on NOVA show that ultra-processed products now dominate the food supplies of various high-income countries and are increasingly pervasive in lower-middle- and upper-middle-income countries. The evidence so far shows that displacement of minimally processed foods and freshly prepared dishes and meals by ultra-processed products is associated with unhealthy dietary nutrient profiles and several diet-related non-communicable diseases. Ultra-processed products are also troublesome from social, cultural, economic, political and environmental points of view. We conclude that the ever-increasing production and consumption of these products is a world crisis, to be confronted, checked and reversed as part of the work of the UN Sustainable Development Goals and its Decade of Nutrition.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".