Intergovernmental Yet Dynamically Expansive: Concordance Legalization as an Alternative Regional Trading Arrangement in ASEAN and Beyond
Bibliographic record
Abstract
Abstract The conventional regional trading arrangement landscape holds two primary models. One is the ‘dynamically expansive supranational model’ of the European Union (EU) that progressively enlarges its community beyond the constituent treaty through its evolving laws and institutions. The other is the ‘static intergovernmental model’ of the United States-Mexico-Canada Agreement (USMCA) where members strictly uphold obligations in the original agreement – no more and no less. A certain genre of Asia-Pacific regional trading arrangements (and beyond in the global South) sits uncomfortably within this bifurcated landscape. Sovereignty-centric, they seek a dynamic and ever-expanding community like the EU but, firmly rejecting supranationalism, insist on intergovernmental modalities as seen in the USMCA. Unsurprisingly, they have not been effective. Using post-2007 integration data from the Association of Southeast Asian Nations, this article presents concordance legalization as a new explanatory framework in this landscape, demonstrating how one can regionalize successfully despite being simultaneously agenda expansive and intergovernmentally operational. Concordance legalization’s four-pronged strategy – the constituent treaty explicitly entrenching intergovernmentalism to facilitate dynamic agenda expansion; the dual-step system of primary and secondary laws (with a carefully calibrated use of hard and soft instruments); the organizational hierarchy that expands, implements and exerts intra-regional accountability pressures through numerous meetings and monitoring mechanisms (rather than adjudication) that enforce compliance – has enabled this curious success.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".