6. Gains from Intra- and Inter-Regional Trade and Economic Co-operation
Bibliographic record
Abstract
INTRODUCTION Despite the progress in tariff reductions initiated by the World Trade Organization (WTO) over the past twenty years, there still exist a number of trade barriers and other problems needed to be addressed. Besides, there are conflicts among WTO country members in several issues. All these delay the trade liberalization process, which will take a long time for trade to be fully liberalized. As a result, several countries have moved ahead of the WTO by initiating regional and/or bilateral free trade agreements (FTAs). By end May 2004, more than 200 agreements have been enforced; 80 per cent of these numbers are bilateral agreements. Recently, ASEAN members have been very active in forming bilateral FTAs with non-ASEAN members. For example, Singapore enacted the Japan-Singapore FTA in 2002 and the U.S.-Singapore FTA in 2003; and Singapore has been negotiating with several countries such as Canada, Mexico, and South Korea. Thailand also signed the free trade agreements with Australia and New Zealand in 2004 and 2005, respectively; and it has been initiating FTAs with several countries. Many concerns have been raised whether it is the most beneficial to each ASEAN member to sign the agreements separately, rather than collectively. Even though a bilateral agreement may broaden market access, reduce trade barriers, increase investment opportunity and strengthen other cooperation between the two countries, it appears that a small country often loses bargaining power over a big country in arranging the agreement. ASEAN, as a group, would have more bargaining powers in negotiating with big FTA partners such as the United States, China, and Japan. Moreover, bilateral FTAs that ASEAN members have engaged in may lead to welfare losses while trade liberalization among ASEAN has yet fully be honoured. These losses are partly caused by in-efficient resource utilization and possible negative terms-of-trade effects, arising from the FTAs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".