School Meals Case Study: Mexico
Bibliographic record
Abstract
Este estudio de caso sobre los programas de alimentación escolar forma parte de una colección liderada por la Comunidad de Práctica "Buenos Ejemplos" del Consorcio de Investigación para la Salud y la Nutrición Escolar. El estudio de caso sobre la alimentación escolar en Mexico tiene como propósito documentar la organización, financiamiento y monitoreo del programa nacional de alimentación escolar en todo el país. Los objetivos de este estudio de caso incluyen presentar una introducción al perfil del país, describir el diseño e implementación de los programas de alimentación escolar, detallar sus procesos de monitoreo y evaluación, y resaltar lecciones aprendidas, buenas prácticas y desafíos. Este estudio de caso se presenta como un documento de trabajo y puede actualizarse para reflejar cambios y circunstancias en evolución. La Comunidad de Práctica "Buenos Ejemplos" apoya la generación de evidencia dentro del Consorcio de Investigación para la Salud y la Nutrición Escolar, que es el brazo de generación de evidencia de la Coalición para la Alimentación Escolar. El objetivo del Consorcio de Investigación es llevar a cabo estudios independientes en diversos sectores y generar evidencia sólida, convincente y aplicable sobre los beneficios de los programas de alimentación escolar, con el fin de respaldar la toma de decisiones basada en evidencia en políticas y prácticas de salud y nutrición escolar This school meals case study forms part of a collection led by the Research Consortium for School Health and Nutrition’s "Good Examples" Community of Practice. The School Meals Case Study of Mexico serves to document how the national school meals programme is organized, funded, and monitored throughout the country. The objectives of this case study include presenting an introduction to the country profile, outlining the design and implementation of school feeding programmes, describing their monitoring and evaluation processes, and highlighting lessons learned, best practices, and challenges. This case study is written as a working paper, and can be updated to reflect evolving circumstances. The ‘Good Examples’ Community of Practice supports the evidence generation of the Research Consortium for School Health and Nutrition, the evidence-generating arm of the School Meals Coalition. The Research Consortium’s objective is to carry out independent research across diverse sectors and generate solid, compelling, and actionable evidence regarding the benefits of school food programs to inform evidence-based decision-making on school health and nutrition policies and practices.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".