Policy insights for national school meals programmes: Annual Research Statements to the members of the School Meals Coalition - 2022, 2023, and 2024
Bibliographic record
Abstract
The scale and near universality of school closures in response to the COVID-19 pandemic highlighted the vital role schools play in protecting the health and wellbeing of learners. This experience strengthened the resolve of countries to re-establish and increase their investment in the education and wellbeing of children as part of building back from the pandemic, resulting in the creation of a global School Meals Coalition (SMC) to help achieve that ambition by 2030. The Research Consortium for School Health and Nutrition was launched in 2021 to provide independent, evidence-based guidance on effective policymaking on school health and nutrition programming to the (now) 109 member states of the Coalition. Guided by a 10-year independent research strategy, the Research Consortium operates as a global network of academics and scholars and consolidates research findings into policy insights that offer actionable approaches to strengthen the quality, efficiency, and coverage of national programmes. With input from over 1,200 Global Academy members from more than 110 countries, the Research Consortium develops an Annual Research Statement on the policy implications of the emerging research in this area, which is presented to policymakers annually at a global convening of the SMC member states. This paper introduces the Research Consortium's Annual Research Statements for years 2022, 2023, and 2024, which report new programmatic and policy insights as well as accumulating and evolving understanding of the research on school health and nutrition.
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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 | 0.113 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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".