A Research on the CPTPP Regulatory Coherence Chapter and USMCA Good Regulatory Practices Chapter
Bibliographic record
Abstract
The Comprehensive and Progressive Agreement for Trans-Pacific Partnership (hereinafter, CPTPP) was signed on March 2018, with the adoption of the “de novo” Chapter on Regulatory Coherence. The CPTPP Regulatory Coherence Chapter has influenced on the modernization of the North American Free Trade Agreement (hereinafter, NAFTA). Later, its name was changed from NAFTA to the United States-Mexico-Canada Agreement (hereinafter, USMCA). The adoption of the USMCA Good Regulatory Practices Chapter was based on the CPTPP Regulatory Coherence Chapter, however, the level of the obligations set out in the former is higher than the latter. It is noteworthy that the Republic of Korea has initiated its Indo-Pacific Economic Framework for Prosperity (hereinafter, IPEF) negotiations from 2022, and that the IPEF Ministerial Statements refers to the “IPEF Good Regulatory Practices Chapter”. Also, the U.S. industries and politicians urge the United States Trade Representative to draft texts on the IPEF Good Regulatory Practices Chapter, based on the CPTPP Regulatory Coherence Chapter and USMCA Good Regulatory Practices Chapter, which reflect the main ideas and legal systems of the United States. For the above-mentioned reasons, this research focuses and analyses on the CPTPP Regulatory Coherence Chapter and USMCA Good Regulatory Practices Chapter, and then points out the IPEF negotiation strategies favorable to the Korean government. This will provide some insights on the prospective IPEF negotiation strategies for the Republic of Korea.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".