Article: Harmonization of FTA Rules of Origin: Examination of General Provisions
Bibliographic record
Abstract
This paper explores whether rules of origin (ROOs) of free-trade agreements (FTAs) are on a path to convergence or divergence. It identifies three major ROOs templates associated with the United States (US), the European Union (EU), and the Association of the Southeast Asian Nations (ASEAN). The study reveals a growing divergence in the general provisions of these templates, with the EU’s template growing more flexible through the introduction of lenient tolerance rules in its FTAs, and the North American template becoming more restrictive, as exemplified by the Net Cost (NC) method and the core-parts rule for automotive products in the ROOs of the United States-Mexico-Canada Agreement (USMCA). Furthermore, the study reveals that the ROOs of the cross-template FTAs that include parties associated with distinct templates adopt the template of the larger economic participant. These cross-template FTAs also introduce novel variations in their general provisions such as the focused-value (FV) method of the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP). The alignment of cross-template FTAs with the major ROOs templates and the introduction of novel variations in cross-template FTAs pose challenges to the global harmonization of FTA ROOs.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".