East Asia’s Engagement with Cosmopolitan Ideals Under its Trade Treaty Dispute Provisions
Bibliographic record
Abstract
An East Asian view about how trade dispute settlement systems should be designed is slowly emerging. Democratically-inspired trade law scholarship and cultural explanations of the international law behaviour of the Southeast and Northeast Asian trading nations have failed to capture or prescribe the actual treaty behaviour of these nations. Instead, such behaviour has resulted in the emergence of two different treaty models for the peaceful settlement of trade disputes. The first, which seems firmly established, may be found in ASEAN’s 2004 dispute settlement protocol and the regimes established under the China-ASEAN, Korea-ASEAN, Japan-ASEAN, and ASEAN-Australia-New Zealand FTAs. A second model, based on the Trans-Pacific Strategic Economic Partnership Agreement, could in time become an alternative model for an Asia-Pacific-wide FTA (i.e., including the East Asian nations within it). It adopts a more open approach; one which better accommodates greater transparency in dispute proceedings. At least for now, the two models coexist, obviating the need for East Asia’s legal policy-makers to choose a clear, dominant design for treaty-based trade dispute settlement in the region. But it also means that East Asia’s trading partners can influence East Asian nations, at least in those trade agreements that—like the Trans-Pacific Partnership Agreement—involve negotiations with trans-continental partners.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".