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Record W2028075888 · doi:10.1177/1468018113484611

Are cash transfers a realistic policy tool for poverty reduction in Sub-Saharan Africa? Evidence from Congo-Brazzaville and Côte d’Ivoire

2013· article· en· W2028075888 on OpenAlexafffund
Anthony Hodges, Geranda Notten, Clare O’Brien, Luca Tiberti

Bibliographic record

VenueGlobal Social Policy · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsUniversité LavalUniversity of Ottawa
FundersUniversity of OttawaUniversité Laval
KeywordsCash transfersPovertyCote d ivoireContext (archaeology)CashDevelopment economicsEconomicsPoverty reductionPoliticsDeveloping countryEconomic growthBusinessPublic economicsGeographyPolitical scienceFinance

Abstract

fetched live from OpenAlex

This article uses evidence from two contrasting African countries, a middle-income oil producer (the Republic of Congo) and a low-income country (Côte d’Ivoire), on the potential role of cash transfers as instruments for poverty reduction and human development. Quantitative simulations of the targeting efficiency, impacts, cost, cost-effectiveness and affordability of different cash transfer options are combined with analysis of political and administrative feasibility. The analysis finds that cash transfers would have more impact on monetary poverty reduction than on human development, while a major practical challenge is to target efficiently in a context of mass poverty. Large-scale cash transfers could be financed domestically in Congo, but this is unlikely in Côte d’Ivoire, and political support is weak in both countries.

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)0.003
metaresearch head score (Gemma)0.013
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 score0.023
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.318
Teacher spread0.290 · 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

Citations10
Published2013
Admission routes2
Has abstractyes

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