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Record W7131100438 · doi:10.5281/zenodo.18750212

E-Voucher Systems and Their Impact on Food Security and Economic Empowerment Amongst Smallholder Women Farmers in Uganda

2002· article· en· W7131100438 on OpenAlexaff
Mulumba Mutebi

Bibliographic record

VenueOpen MIND · 2002
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsFood securityEmpowermentSubsidyPaymentAgricultureImpact evaluationEconomic impact analysisFood systems

Abstract

fetched live from OpenAlex

E-voucher systems are digital payment platforms used to distribute subsidies or benefits electronically. A systematic review of peer-reviewed articles from to was conducted using databases such as PubMed and Google Scholar. Studies assessing the impact of e-vouchers were included if they focused specifically on smallholder women farmers in Uganda. The analysis revealed a significant increase (p<0.05) in food security levels among participants who received e-vouchers, with an average improvement rate of 23%. E-voucher systems have the potential to enhance economic empowerment and food security for smallholder women farmers in Uganda. Further research should explore long-term effects and cost-effectiveness of these systems. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.

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 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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.039
GPT teacher head0.266
Teacher spread0.227 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2002
Admission routes1
Has abstractyes

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