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Taking Stock of Poverty Reduction Efforts in Nigeria

2011· article· en· W1578644227 on OpenAlexvenueno aff
Flora O. Ntunde, Chukwuemeka O. Oteh

Bibliographic record

VenueCanadian social science · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic, financial, and policy analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyPoverty reductionEconomic growthState (computer science)Development economicsStock (firearms)SocioeconomicsPoor peopleBusinessEconomicsPolitical scienceGeography

Abstract

fetched live from OpenAlex

This study is an evaluation of the Poverty Eradication Programmes in Nigeria. It tries to assess their effectiveness in helping to improve on the lives of the poor. Primary data were collected through questionnaire administered to randomly selected adult male and female residents in Enugu State of Nigeria. Secondary data were collected from the Poverty Eradication office in the State. The analysis shows that most of the poverty reduction efforts had no significant impact on the lives of the poor. Even those that were recorded as effective had negligible impact on the populace to have reduced poverty. The study enumerates among others inadequate funding, mismanagement of resources and inadequate infrastructures as problems stifling most poverty alleviation programmes in Nigeria. The study recommends that in addition to establishing these Poverty alleviation programmes, Nigeria should strive to move away from import dependent economy to an export oriented one. Key words: Poverty eradication programmes; Nigeria; Effectiveness

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)0.006
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 score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.236
Teacher spread0.188 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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