ASEAN Economic Cooperation: Trade Liberalization Impacts on the National Economy
Bibliographic record
Abstract
This study aims to analyse the impact of trade liberalization by focusing on twelve priority industrial sectors in the ASEAN-5 (Singapore, Thailand, Indonesia, Malaysia and the Philippines). The General Equilibrium Model based on Multi-country Input Output Data as provided by the GTAP is used to measure potential economic benefits of reducing tariffs on output, trade balance, welfare gain, and competitiveness. We compare the outcome of the CGE approach with the Cost Benefit Analysis (CBA) based on the Analytical Hierarchy Process (AHP). The results show that the outcomes of the CGE Model does not match those suggested by the AHP. However, they do support the findings of Falianty (2005), Achsani and Siregar (2010), Achsani and Partisiwi (2010), Nugroho and Yanfitri (2011). Our results suggest that taking non-economic but relevant factors from public opinion into account affects the robustness of CGE studies based purely on economic factors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".