US hegemony and international organizations : the United States and multilateral institutions
Bibliographic record
Abstract
Contributors to this volume - Ralph A. Cossa is President of Pacific Forum CSIS, Honolulu and Washington David G. Haglund is Professor in the department of Political Studies, Queen's University, Kingston, Ontario Stephen Hopgood is Lecturer in International Politics at the School of Oriental and African Studies, University of London G. John Ikenberry is Peter F. Krogh Professor of Geopolitics and Global Justice at Gerogetown University Hal Klepak is Professor of Latin American Diplomatic and Military History at the Royal Military College of Canada Edward C. Luck is Director of the Center on International Organization at the School of Internaitonal and Public Affairs, Columbia University David Malone is President of the International Peace Academy in New York and in the Canadian Foreign Service Philip Nel was until 2002 Chair of the Department nad Professor of Political Science at the University of Stellenbosch, South Africa, and from 2003 Professor of Political Studies at the University of Otago, New Zeland Gautam Sen is Lecturer in the Politics of the World Economy, Department of International Relations, LSE, Great Britain Ngaire Woods is Fellow in Politics and International Relations at University College, Oxford 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.000 |
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.
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".