Pacific Alliance, CPTPP and USMCA investment chapters: Substantive convergence, procedural divergence
Bibliographic record
Abstract
This article compares the investment chapters of the Additional Protocol to the Framework Agreement of the Pacific Alliance (PA-AP), the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) and the United States-Mexico-Canada Agreement (USMCA). Our objective is to determine their degree of normative convergence. We conclude that these investment chapters include very similar substantive rules and principles on international investments in terms of definitions, the rules’ scope of application, treatment standards (national treatment and most favored nation treatment), absolute standards (international minimum standard of treatment, fair and equitable treatment, and full protection and security), investment protection rules (direct and indirect expropriation, compensation, and transfers), and performance requirements. We also conclude that these investment chapters differ, in some respects very strongly, regarding investor-State dispute settlement (ISDS). First, TPP and USMCA rules are often similar and frequently diverge from PA-AP rules. Second, party coverage and protection coverage diverge strongly between the USMCA vis-a-vis the PA-AP and CPTPP. Thus, as a consequence of substantive convergence and strong procedural divergence, we argue that complainants will most likely choose the forum between the PA-AP, CPTPP and USMCA according to procedural reasons.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".