Revealing the truth about nutrition labelling: The age of the confused consumer
Bibliographic record
Abstract
Inadequate nutrition is considered to be a leading cause of mortality in the world. Access to quality information about the nutritional quality of food products is essential for making informed decisions in terms of food selection. To this end, nutrition labelling can be used as a tool for helping guide consumers food choices. This review article will examine which consumers use nutrition labels and what types of information they get from them; how nutrition labels influence health choices, dietary habits, and consumer behaviours; and finally, current practices in nutrition labelling and whether this aligns with consumer preferences. Findings from this article are useful for developing methods of nutritional label education and understanding how nutrition labels can be improved to become more useful to consumers. Keywords: health; nutrition labelling; food products; packaging; public education; consumer perception
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 | 0.026 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".