Beyond U.S.-China Rivalry: Rule Breaking, Economic Coercion, and the Weaponization of Trade
Bibliographic record
Abstract
Growing U.S.-China rivalry undoubtedly poses a profound threat to the multilateral trading system. In “Governing the Interface of U.S.-China Trade Relations,” Gregory Shaffer provides a highly nuanced and balanced analysis of the nature of this threat and potential solutions to address it. Yet managing trade conflict between the United States and China is, I argue, only one of the twin challenges currently facing the multilateral trading system. The other is how to rein in growing economic coercion and the arbitrary abuse of power by dominant states in the system. The United States and China have each become highly disruptive forces in the liberal trading order—not simply because of their bilateral trade relations but also, and just as importantly, because of their behavior toward the rest of the world. Both of these countries have increasingly turned away from trade multilateralism and toward aggressive unilateralism and the raw use of coercive power in their dealings with other states. It is this flagrant disregard for the rule of law on the part of the system's two dominant powers that has thrown the World Trade Organization (WTO) into crisis and ultimately poses the greatest threat to the global trade regime.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".