Inseguridad alimentaria y del agua
Bibliographic record
Abstract
OBJETIVO: Analizar la inseguridad del agua (IAg), frecuencia del suministro de agua (FSA) e inseguridad alimentaria (IA) en hogares mexicanos, abordando sus determinantes sociales y aportar recomendaciones para las políticas públicas. Material y métodos. Se analizó la información de 28 500 hogares de la Encuesta Nacional de Salud y Nutrición (Ensanut Continua 2020-2023). Se aplicaron escalas de experiencias validadas como HWISE y ELCSA, para medir la IAg e IA, así como un indicador sobre FSA, de acuerdo con algunos determinantes. RESULTADOS: 16% de los hogares mexicanos experimentan IAg y 22% padecen IA moderada y severa. Sólo 34.7% recibe agua las 24 horas todos los días. Los determinantes de los hogares más afectados por la IAg, IA y en FSA son las peores condiciones de bienestar, ser indígena y cuando la IA e IAg se encuentran juntas en los hogares. CONCLUSIONES: Es imprescindible acelerar la aplicación y mejorar la cobertura de acciones que impacten positivamente en el acceso y disponibilidad de agua y alimentos de las personas vulnerables.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".