Trade Structure and the Effectiveness of America's "Aggressively Unilateral" Trade Policy
Bibliographic record
Abstract
When does America's “aggressively unilateral” trade policy work best to open overseas markets? This paper revisits the determinants and effectiveness of Section 301 of U.S. trade law and develops a modified two-level game model for understanding the conditions under which domestic interests and institutions support the use of aggressive negotiation tactics. It argues that a system-level variable, the structure of trade, systematically affects threat effectiveness by influencing both the level of unity among domestic interest groups and the degree of divided government in the sender of threats (the United States). America's sanction threats will enjoy more unified domestic support and hence be more credible when the dispute involves a country having a competitive trade relationship with the U.S. (such as Japan, Canada, and the European Union) rather than one having a complementary trade relationship with the U.S. (such as China, India, and Brazil). Statistical tests based on the universe of Section 301 cases concluded between 1975 and 1995 yield evidence in support of this contention.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".