An alliance for open trade: How to counter Trump's tariffs
Bibliographic record
Abstract
Trump's renewed tariffs on traditional allies-including the EU, Canada, Mexico, and Brazil-signal a return to aggressive protectionism, openly disregarding WTO rules and threatening the stability of the global trade system. The authors argue that unilateral retaliation by individual countries is unlikely to be effective; instead, only a coordinated response by a broad coalition of affected nations-such as the EU, Canada, Mexico, Brazil, and South Korea-can exert meaningful economic pressure on the United States. This policy brief proposes that such a joint response should remain WTO-compliant, target politically sensitive sectors of the U.S. economy (including automobiles, pharmaceuticals, and agriculture), and be framed not as punitive, but as a principled defense of the rules-based international trade order. Timely and coordinated action is critical, as further delays risk deepening fragmentation and inflicting long-term damage on the multilateral trading system.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".