Conflict or cooperation? The successful resolution of U.S. trade disputes.
Bibliographic record
Abstract
Since the end of WWII, the liberal trade regime has seen an unprecedented rate of expansion and growth. Trade between the U.S.A. and its largest trading partners has been at the forefront of this expansion. With this growth in trade has come changes in the nature of trade disputes. This growth and change, though, has resulted in a system of dispute resolution that varies among the U.S.A.'s trade partners through governmental behavior in the resolution of these disputes. The evolution of trade dispute behavior is evident in the U.S.A.'s use of both domestic and international trade dispute mechanisms, specifically anti-dumping, countervailing duty, Section 301, GATT, and the U.S.-Canada Free Trade Agreement mechanisms. This study examines variance in the U.S.A.'s use of these trade dispute mechanisms with Canada, the European Union, and Japan from 1979--1994 using cross-sectional data and statistical analysis. This study finds that the target country argument, developed herein, explains U.S. trade dispute behavior. The target country argument posits that the use of specific trade dispute mechanisms and the degree of conflict escalation is dependent upon the past relationship with that specific trade partner. Put most simply, the U.S.A.'s use of trade dispute mechanisms varies with the trade dispute partner and the information gathered in each dispute is context specific. Contrary to conventional wisdom, it is found that variance in trade dispute resolution is not explained solely by either interdependence or to domestic interest groups.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".