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Record W3041397125 · doi:10.18192/potentia.v4i0.4397

Social Protection of the Poor in Africa

2012· article· en· W3041397125 on OpenAlexaffvenue
Olabanji Akinola

Bibliographic record

VenuePotentia Journal of International Affairs · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSocial protectionPovertyConditional cash transferEconomic growthGovernment (linguistics)Cash transfersDevelopment economicsPolitical scienceSustainabilityPoliticsChronic povertyDeveloping countryBusinessEconomicsPoverty reduction

Abstract

fetched live from OpenAlex

Conditional cash Transfers (CCTS) in the past decade have become attractive as social protection programmes for reducing chronic poverty and vulnerabilities in poor African households. however, the adoption of CCTS in african countries overlooks and neglects the individual and different programme contexts required for successful implementation of the programmes. This negligence can impede the achievement of programme objectives as well as their sustainability owing to prevailing socio-political together with economic development constraints. This policy brief thus advocates for greater consideration by government officials and their international development partners of the needs of individual countries in the design and implementation of ccT programmes in africa. While various social protection programmes exist in one (un)conditional form or the other in countries like Ghana, Nigeria, Ethiopia, South Africa, Zambia, Egypt, and Uganda amongst others, the introduction of CCTS as social protection programmes is a relatively new phenomena and therefore throws up some challenges. The challenges they present should therefore be seen as part of a learning process rather than reasons to avoid attempting to implement them successfully.

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)0.001
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 score0.292
Threshold uncertainty score0.256

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 communication0.0000.000
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 head0.021
GPT teacher head0.277
Teacher spread0.256 · 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 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

Citations0
Published2012
Admission routes2
Has abstractyes

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