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Record W2023710566 · doi:10.1080/10220461.2010.534604

Social challenges and progress in IBSA

2010· article· en· W2023710566 on OpenAlexaff
Manmohan Agarwal, Hany Besada, Lyal White

Bibliographic record

VenueSouth African Journal of International Affairs · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsThe North South Institute
Fundersnot available
KeywordsPolitical scienceCivil societyPovertyEconomic growthContext (archaeology)Social changePublic administrationSocial WelfareDevelopment economicsPublic relationsPolitical economySociologyPoliticsEconomicsLaw

Abstract

fetched live from OpenAlex

The India–Brazil–South Africa (IBSA) Dialogue Forum was launched in June 2003 to push for these countries' bids for a place on the United Nation' Security Council, but IBSA's attention has shifted over time towards development and economic reform. This article discusses the progress in addressing social development challenges within the member countries of IBSA. It examines the social achievements of IBSA members, in the context of their economic performance and the Millennium Development Goals. It also assesses the forces which propel these societies' social policies, especially the influence of civil society, and whether there has been benefit in this regard in their collaboration within the IBSA forum. Their experiences show the critical importance of civil society in design and execution of programmes directed towards the poor, an important factor to be kept in mind by multilateral and bilateral agencies involved in poverty alleviation projects in developing countries. The three nations could cooperate to share ideas for effective social welfare programmes, and join together in multilateral forums to form a powerful voice for change.

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)0.011
metaresearch head score (Gemma)0.012
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: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0180.013
Scholarly communication0.0110.006
Open science0.0010.019
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0100.001

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 head0.020
GPT teacher head0.293
Teacher spread0.273 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

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