Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".