Monitoring the price and affordability of foods and diets globally
Bibliographic record
Abstract
Food prices and food affordability are important determinants of food \nchoices, obesity and non-communicable diseases. As governments around \nthe world consider policies to promote the consumption of healthier foods, \ndata on the relative price and affordability of foods, with a particular focus \non the difference between ‘less healthy’ and ‘healthy’ foods and diets, are \nurgently needed. This paper briefly reviews past and current approaches to \nmonitoring food prices, and identifies key issues affecting the development \nof practical tools and methods for food price data collection, analysis and \nreporting. A step-wise monitoring framework, including measurement indicators, \nis proposed. ‘Minimal’ data collection will assess the differential \nprice of ‘healthy’ and ‘less healthy’ foods; ‘expanded’ monitoring will assess \nthe differential price of ‘healthy’ and ‘less healthy’ diets; and the ‘optimal’ \napproach will also monitor food affordability, by taking into account \nhousehold income. The monitoring of the price and affordability of \n‘healthy’ and ‘less healthy’ foods and diets globally will provide robust data \nand benchmarks to inform economic and fiscal policy responses. Given the \nrange of methodological, cultural and logistical challenges in this area, it is \nimperative that all aspects of the proposed monitoring framework are \ntested rigorously before implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".