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Record W3129000967

Anti-Corruption Agenda of the G20: Bringing Order without Law

2020· article· en· W3129000967 on OpenAlexaboutno aff
Tanu M. Goyal

Bibliographic record

VenueRePEc: Research Papers in Economics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsLanguage changeOrder (exchange)Political scienceLaw and economicsLawEconomics
DOInot available

Abstract

fetched live from OpenAlex

The G20 has emerged as a premier deliberative forum, involving leaders of some of the largest, systematically important countries of the world. Over the years, the G20 agenda has evolved to include pertinent issues for both developed countries and the emerging market economies. After the G20 Summits were launched, certain global concerns that required collective action became permanent features of the G20 agenda. Tackling corruption was one of them. Corruption is being characterised as an international problem requiring collective corrective action. The G20 established an Anti-Corruption Working Group as early as in 2010, at the fourth summit in Toronto, which sets the Anti-Corruption Action Plan for the G20 members. Over the years, the issues under the Working Group have evolved to capture continuous and emerging challenges for the G20 members. India has been actively involved in the anti-corruption agenda of the G20 and has periodically submitted its implementation reports to the G20. More recently, India has also contributed to the anti-corruption agenda by making suggestion on significant issues for G20 members. This paper discusses the evolution of the anti-corruption agenda globally and within the G20, and India's progress and contribution in furthering this agenda. While the objective of this paper is to report the progress of the G20 members including India, the paper also makes observations regarding the G20 as a multilateral body for addressing anti-corruption issues. The paper is based on a review of the G20 documents and discussions with experts in the area.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.426

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 head0.068
GPT teacher head0.347
Teacher spread0.279 · 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 designNot applicable
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

Citations1
Published2020
Admission routes1
Has abstractyes

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