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Record W2167011574 · doi:10.1002/pop4.62

International Organizations and Ideas About Poverty in Sub‐Saharan Africa

2014· article· en· W2167011574 on OpenAlexafffund
Rosina Foli, Daniel Béland

Bibliographic record

VenuePoverty & Public Policy · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversity of Saskatchewan
FundersCanada Research ChairsWorld Bank Group
KeywordsPovertyConditionalityCivil societyPolitical sciencePerspective (graphical)Development economicsEconomic growthInequalityRegional scienceSociologyEconomicsPolitics

Abstract

fetched live from OpenAlex

This study explores how international organizations (IOs) shape ideas about poverty and anti‐poverty policymaking in sub‐Saharan Africa (SSA). It argues that, beyond the use of conditionality, IOs significantly influence conceptions of poverty in SSA through various mechanisms, including technical assistance, personnel training, and capacity building, collaborating with civil society organizations, publications, conferences, seminars, and as think tanks. The analysis focuses on the World Bank (WB) and the Organization for Economic Co‐operation and Development (OECD), two organizations that have had a long‐standing relationship with SSA countries and have made significant contributions to development in the region. However, unlike the WB, whose activities in SSA are well known, the OECD's role in SSA is much less known. Therefore, this study broadens the discussion of the role of IOs in domestic policy development in SSA by incorporating the OECD. At the same time, the study offers a comparative perspective missing from empirical studies about IOs, which tend to focus on only one organization at a time.

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)0.006
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0070.016
Scholarly communication0.0090.007
Open science0.0000.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.278
Teacher spread0.264 · 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 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

Citations24
Published2014
Admission routes2
Has abstractyes

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