Bibliographic record
Abstract
The NOVA food categorisation recommends 'avoiding processed foods (PF), especially ultra-processed foods (UPF)' and selecting minimally PF to address obesity and chronic disease. However, NOVA categories are drawn using non-traditional views of food processing with additional criteria including a number of ingredients, added sugars, and additives. Comparison of NOVA's definition and categorisation of PF with codified and published ones shows limited congruence with respect to either definition or food placement into categories. While NOVA studies associate PF with decreased nutrient density, other classifications find nutrient-dense foods at all levels of processing. Analyses of food intake data using NOVA show UPF provide much added sugars. Since added sugars are one criterion for designation as UPF, such a proof demonstrates a tautology. Avoidance of foods deemed as UPF, such as wholegrain/enriched bread and cereals or flavoured milk, may not address obesity but could decrease intakes of folate, calcium and dietary fibre. Consumer understanding and implementation of NOVA have not been tested. Neither have outcomes been compared with vetted patterns, such as Dietary Approaches to Stop Hypertension, which base food selection on food groups and nutrient contribution. NOVA fails to demonstrate the criteria required for dietary guidance: understandability, affordability, workability and practicality. Consumers' confusion about definitions and food categorisations, inadequate cooking and meal planning skills and scarcity of resources (time, money), may impede adoption and success of NOVA. Research documenting that NOVA can be implemented by consumers and has nutrition and health outcomes equal to vetted patterns is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.073 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".