Measuring food access using least-cost diets: Results for global monitoring and targeting of interventions to improve food security, nutrition and health
Bibliographic record
Abstract
Benchmark diets using the most affordable locally available items to meet health and nutrition needs have long been used to guide food choice and nutrition assistance. This paper describes the result of recent innovations scaling up the use of such least-cost diets by UN agencies, the World Bank, and national governments for a different purpose, which is monitoring food environments and targeting systemic interventions to improve a population's access to sufficient food for an active and healthy life. Measuring food access using least-cost diets allows a clearer understanding of where poor diets are caused by unavailability or high prices for even the lowest-cost healthy foods, insufficient income or other resources to acquire those foods, or the use of other foods instead due to reasons such as time use and meal preparation costs, or cultural factors such as taste and aspirations. This paper reviews the data, methods and results that have led to official FAO and the World Bank adoption of cost and affordability metrics for global monitoring, and the parallel use of similar methods to guide interventions in country studies led by the World Food Programme with partner agencies across Africa, Asia and Latin America. We conclude by summarizing how increasing availability of food price data, matched to food composition and dietary requirements, allows analysts to use recently developed software tools for least-cost diet assessment to improve food access in a wide range of settings.
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".