From CPTPP to US–Taiwan Initiative on 21st-Century Trade: The Evolution of Good Regulatory Practices and Its Implications for Taiwan
Bibliographic record
Abstract
This paper examines the evolution and implications of Good Regulatory Practices (GRP) – otherwise known as ‘regulatory coherence’ in international trade agreements, focusing on the US-Taiwan Initiative on twenty-first-Century Trade. By comparing GRP frameworks in the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP), the United States–Mexico–Canada Agreement (USMCA), and the US–Taiwan Initiative, the paper highlights the challenges of integrating GRP into Taiwan’s legal system. The analysis reveals significant disparities between GRP requirements and Taiwan’s existing Administrative Procedure Act (APA) in key areas such as public consultation, regulatory impact assessments (RIAs), and sunset review mechanisms. The paper also explores potential ramifications for Taiwan’s export control regime, particularly for dual-use technologies like semiconductors, revealing tensions between GRP’s transparency and public consultation demands and confidentiality needs. While GRP aims to enhance regulatory quality and facilitate trade, its implementation in Taiwan necessitates complex legislative adjustments. This paper provides a foundation for future research on aligning Taiwan’s legal framework with GRP principles, considering its unique blend of US and German legal influences. The findings have broader implications for understanding the complexities of implementing GRP across diverse legal and administrative systems globally.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".