Global Leadership in Transition: Making the G20 More Effective and Responsive
Bibliographic record
Abstract
Global Leadership in Transition calls for innovations that would institutionalize or consolidate the G20, helping to make it the global economy's steering committee. The emergence of the G20 as the world's premier forum for international economic cooperation presents an opportunity to improve economic summitry and make global leadership more responsive and effective, a major improvement over the G8 era. Key contributors to this volume were well ahead of their time in advocating summit meetings of G20 leaders. In this book, they now offer a rich smorgasbord of creative ideas for transforming the G20 from a crisis-management committee to a steering group for the international system that deserves the attention of those who wish to shape the future of global governance. --C. Randall Henning, American University and the Peterson Institute Contributors: Alan Beattie, Financial Times ; Thomas Bernes, Centre for International Governance Innovation (CIGI); Sergio Bitar, former Chilean minister of public works; Paul Blustein, Brookings Institution and CIGI; Barry Carin, CIGI and University of Victoria; Andrew F. Cooper, CIGI and University of Waterloo; Kemal Dervis, Brookings; Paul Heinbecker, CIGI and Laurier University Centre for Global Relations; Oh-Seok Hyun, Korea Development Institute (KDI); Jomo Kwame Sundaram, United Nations; Homi Kharas, Brookings; Hyeon Wook Kim, KDI; Sungmin Kim, Bank of Korea; John Kirton, University of Toronto; Johannes Linn, Brookings and Emerging Markets Forum; Pedro Malan, Itau Unibanco; Thomas Mann, Brookings; Paul Martin, former prime minister of Canada; Simon Maxwell, Overseas Development Institute and Climate and Development Knowledge Network; Jacques Mistral, Institut Francais des Relations Internationales; Victor Murinde, University of Birmingham (UK); Pier Carlo Padoan, OECD Paris; Yung Chul Park, Korea University; Stewart Patrick, Council on Foreign Relations; Il SaKong, Presidential Committee for the G20 Summit; Wendy R. Sherman, Albright Stonebridge Group; Gordon Smith, Centre for Global Studies and CIGI; Bruce Stokes, German Marshall Fund; Ngaire Woods, Oxford Blavatnik School of Government; Lan Xue, Tsinghua University (Beijing); Yanbing Zhang, Tsinghua University.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".