Rising States, Rising Institutions: Challenges for Global Governance
Bibliographic record
Abstract
The global order is shifting. Even though no major war has intervened to reshape the architecture of the international order, the global financial crisis has accentuated the emergence of an enlarged global leadership. It is clear that change is afoot. The United States may be hanging on as the world's leading power, as the European Union remains an independent force in global politics, but a host of rising states -including China, India, and Brazil -clamor to be heard and take on bigger roles in world forums. Rising States, Rising Institutions features a panel of distinguished scholars who examine the forces at work: Gregory Chin (York University), Daniel W. Drezner (Tufts University), Thomas Hale (Princeton University), Andrew Hurrell (Oxford University), G. John Ikenberry (Princeton University), John Kirton (University of Toronto), Flynt Leverett (New America Foundation), Steven E. Miller (Harvard University), Andrew Moravcsik (Princeton University), Amrita Narlikar (Cambridge University), and Anne-Marie Slaughter (U.S. State Department). Together they analyze different models of international cooperation, the states that have most actively challenged the existing order, and leading and emergent international institutions such as the G-20, the nascent regime for sovereign wealth funds, the International Atomic Energy Agency, and the entities organized to foster cooperation in the war on terror.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".