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Cash water expenditures are associated with household water insecurity, food insecurity, and perceived stress in study sites across 20 low- and middle-income countries

2019· article· en· W2994276456 on OpenAlexaff
Justin Stoler, Amber L. Pearson, Chad Staddon, Amber Wutich, Elizabeth A. Mack, Alexandra Brewis, Asher Y. Rosinger, Ellis Adjei Adams, Mallika Alexander, Mobolanle Balogun, Michael J. Boivin, Genny Carrillo, Kelly Chapman, Stroma Cole, Shalean M. Collins, Jorge Escobar-Vargas, Matthew C. Freeman, Gershim Asiki, Hala Ghattas, Ashley Hagaman, Zeina Jamaluddine, Wendy Jepson, Kenneth Maes, Jyoti S. Mathad, Hugo Melgar‐Quiñonez, Joshua D. Miller, Monet Niesluchowski, Nasrin Omidvar, Luisa Samayoa-Figueroa, Ernesto C. Sánchez-Rodríguez, Marianne V. Santoso, Roseanne C. Schuster, Andrea Sullivan, Yihenew Tesfaye, Nathaly Triviño, Alex Trowell, Désiré Tshala-Katumbay, Raymond Asare Tutu, Sera L. Young, Hassan Eini‐Zinab

Bibliographic record

VenueThe Science of The Total Environment · 2019
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMcGill University
FundersNational Institute of Mental HealthNational Institute of Environmental Health SciencesInnovative Methods and Metrics for Agriculture and Nutrition ActionsNational Science Foundation
KeywordsPovertyPsychological interventionFood insecurityHousehold incomeCashTobit modelDemographic economicsEconomicsSocioeconomicsFood securityBusinessPsychologyGeographyAgricultureEconomic growth

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.222
Teacher spread0.209 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations115
Published2019
Admission routes1
Has abstractno

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