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Record W2521475848 · doi:10.1093/wber/lhw042

Shoeing the Children: The Impact of the TOMS Shoe Donation Program in Rural El Salvador

2016· article· en· W2521475848 on OpenAlexaff
Bruce Wydick, Elizabeth Katz, Flor Calvo, Felipe Castro Gutiérrez, Brendan Janet

Bibliographic record

VenueThe World Bank Economic Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsDonationAttendanceContext (archaeology)Test (biology)MedicineDemographyEnvironmental healthGerontologyPsychologyGeographyEconomic growthEconomics

Abstract

fetched live from OpenAlex

We carry out a cluster-randomized trial among 1,578 children from 979 households in rural El Salvador to test the impacts of TOMS shoe donations on children's time allocation, school attendance, health, self-esteem, and aid dependency.Results indicate high levels of usage and approval of the shoes by children in the treatment group, and time diaries show modest evidence that the donated shoes allocated children's time toward outdoor activities.Difference-in-difference and ANCOVA estimates find generally insignificant impacts on overall health, foot health, and self-esteem but small positive impacts on school attendance for boys.Children receiving the shoes were significantly more likely to state that outsiders should provide for the needs of their family.Thus, in a context where most children already own at least one pair of shoes, the overall impact of the shoe donation program appears to be negligible, illustrating the importance of more careful targeting of in-kind donation programs.

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.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.319
Teacher spread0.305 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

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