Rules of Origin Within ASEAN and RCEP: Has It Been Resolved?
Bibliographic record
Abstract
As a well-established trading bloc, rules of origin (ROO) for ASEAN are expected to benefit member states’ businesses by providing preferential tariff treatment which is the fundamental purpose of trade liberalization. However, these rules are prevented from being applied thoroughly due to the lack of government trust in certain member states in business actors and targets of duty collection for customs authorities. The self-certification by business actors has proven to be a solution to the problem but ASEAN A-X Formula may be counterproductive to ROO, eliminating the foundation of regionalization and potentially causing trade deflection. The First Protocol of ASEAN Trade in Goods Agreement (ATIGA) amendment simplifies the procedure of operational certification for ROO. Furthermore, the Regional Comprehensive Economic Partnership (RCEP) as a significant trading pact including ASEAN members and five major trading partners has adopted proof of origin allowing self-certification applied earlier in the European Union and NAFTA. Alternative solutions should also be explored since distrust and national financial interests have not been resolved. Recently, blockchain embedded with smart contracts has been applied in various business sectors which should be further applied in free trade area (FTA) applications. Blockchain’s characteristics as an immutable ledger originating from the hashing process and cryptography would address the problem of ASEAN ROO application. Therefore, this study aimed to analyze the effectiveness of ROO within ASEAN and RCEP. A literature review and doctrinal study methodology were applied in this analysis.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".