Monitoring foods and beverages provided and sold in public sector settings
Bibliographic record
Abstract
This paper outlines a step-wise framework for monitoring foods and beverages provided or sold in publicly funded institutions. The focus is on foods in schools, but the framework can also be applied to foods provided or sold in other publicly funded institutions. Data collection and evaluation within this monitoring framework will consist of two components. In component I, information on existing food or nutrition policies and/or programmes within settings would be compiled. Currently, nutrition standards and voluntary guidelines associated with such policies/programmes vary widely globally. This paper, which provides a comprehensive review of such standards and guidelines, will facilitate institutional learnings for those jurisdictions that have not yet established them or are undergoing review of existing ones. In component II, the quality of foods provided or sold in public sector settings is evaluated relative to existing national or sub-national nutrition standards or voluntary guidelines. Where there are no (or only poor) standards or guidelines available, the nutritional quality of foods can be evaluated relative to standards of a similar jurisdiction or other appropriate standards. Measurement indicators are proposed (within 'minimal', 'expanded' and 'optimal' approaches) that can be used to monitor progress over time in meeting policy objectives, and facilitate comparisons between countries.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".