A Really Big Button That Doesn’t Do Anything? The Anti-NME Clause in US Trade Agreements Between Law and Geoeconomics
Bibliographic record
Abstract
Abstract The United States-Mexico-Canada Agreement (USMCA) features a clause, dubbed ‘anti-China’, which sets out legal consequences in case one of the parties negotiates or enters into a free trade agreement (FTA) with a nonmarket economy (NME). A similarly worded objective appears among the negotiating objectives of the US for FTAs with the European Union, Japan, and the United Kingdom. This article examines the anti-NME clause, arguing that its concrete legal consequences are less relevant than its symbolic effects. The USMCA clause itself is difficult to replicate in bilateral agreements, since it depends on cooperation between the two nonsigning parties. Its operation is nonetheless similar to that of two unilateral remedies available under the law of treaties, permitting a reasonable assessment that the clause, if it follows its original design, will aim to permit termination of bilateral US FTAs in response to the other party entering into an NME FTA. While such a clause would offer little in terms of concrete effects if added to agreements that already permit unilateral withdrawal, its greatest value may not be in its legal effects but in its legitimating and signaling properties, which push USMCA parties to establish a common front in the ‘geoeconomic’ dispute between the United States and China.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| 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".