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Record W1986647192 · doi:10.1080/19439342.2011.626059

How can sanitary infrastructures reduce child malnutrition and health inequalities? Evidence from Guatemala

2011· article· en· W1986647192 on OpenAlex
Thomas G. Poder, Jie He

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Development Effectiveness · 2011
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversité de SherbrookeHôpital Fleurimont
Fundersnot available
KeywordsMalnutritionPovertyEconomic growthEnvironmental healthInequalityBusinessPromotion (chess)Propensity score matchingSanitationPublic healthChild mortalityImpact evaluationEconomicsDevelopment economicsSocioeconomicsDeveloping countryMedicinePolitical science

Abstract

fetched live from OpenAlex

With the propensity score matching method, we carried out an average benefit incidence analysis that helps disclose those who really benefited from the sanitary services in Guatemala. Specifically, we tested the role of income, maternal education and social capital on how sanitary infrastructures affect child health. Results indicated that the child health benefits from infrastructure increase (decrease) with the household's socio-economic status when the infrastructure is a complement (substitute) of the private inputs provided by the household, and that the role of the infrastructure (complement or substitute) itself depends on the household's socio-economic status. Finally, results revealed that the battle against child malnutrition and health inequalities could be improved by combining sanitary infrastructure investments with effective public promotion of maternal education, social trust, and poverty reduction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.296
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it