Investing in school systems: conceptualising returns on investment across the health, education and social protection sectors
Bibliographic record
Abstract
Public policies often aim to improve welfare, economic injustice and reduce inequality, particularly in the social protection, labour, health and education sectors. While these policies frequently operate in silos, the education sphere can operate as a cross-sectoral link. Schools represent a unique locus, with globally hundreds of millions of children attending class every day. A high-profile policy example is school feeding, with over 400 million students worldwide receiving meals in schools. The benefits of harmonising interventions across sectors with a common delivery platform include economies of scale. Moreover, economic evaluation frameworks commonly used to assess policies rarely account for impact across sectors besides their primary intent. For example, school meals are often evaluated for their impact on nutrition, but they also have educational benefits, including increasing attendance and learning and incorporating smallholder farmers into corporate value chains. To address these gaps, we propose the introduction of a comprehensive value-for-money framework for investments toward school systems that acknowledges the return to a common delivery platform-schools-and the multisectoral returns (eg, education, health and nutrition, labour, social protection) emerging from the rollout of school-based programmes. Directly building on benefit-cost analysis methods, this framework could help identify interventions that yield the highest gains in human capital per budget expenditure, with direct implications for finance ministries. Given the detrimental impact of COVID-19 on schoolchildren and human capital, it is urgent to build back stronger and more sustainable welfare systems.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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".