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Aid and Poverty in Africa: Do Well‐being Measures Understate the Progress?

2010· article· en· W2119668540 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAfrican Development Review · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPovertyStochastic dominanceEconomicsDevelopment economicsDeveloping countryDominance (genetics)Demographic economicsEconomic growthEconometrics

Abstract

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Abstract : In the last 15 years international aid donors to Africa have shifted their focus dramatically toward health and education; the share of social sector support in total aid rose from 33 per cent to 60 per cent from 1990–94 to 2000–2004 alone. If this aid has been effective, it is unlikely to be captured in GDP or income poverty figures. This paper uses the Demographic and Health Survey at multiple points in time to explore changes in well‐being in ten sub‐Saharan African countries. It compares the evolution of both assets and health which are considered as the two main dimensions of well‐being. These dimensions are simultaneously estimated using the structural equation models with latent variables that have been developed in the psychometric literature. The comparisons of well‐being across time in each country are based on the stochastic dominance analysis. The main results suggest that assets and health have improved during the last two decades in most of these countries. A decline in assets is observed for three countries while health deteriorates in two countries. The reduced poverty appears to be explained less by the aid than other factors in most cases.

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.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.024
GPT teacher head0.278
Teacher spread0.254 · 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