Japan's participation in TPP negotiation Prospect and policy implications for Korea
Bibliographic record
Abstract
Since March 2010, the Trans-Pacific Strategic Economic Partnership Agreement (TPP) negotiations have been taking place, where members have expanded into the 11 Asia-Pacific countries from the original Pacific 4 (P4: Singapore, New Zealand, Chile, and Brunei). The P4 has rooted in the P5, where the United States initiated the liberalization of trade and investment among only P5 countries (the United States, Australia, Singapore, New Zealand, and Chile) to overcome the limits of APEC’s open regionalism in the late 1990s. However, the United States and Australia could not join the negotiations because of their respective domestic political problems. The current TPP negotiation is said to have been formally launched in March 2010 when the United States joined the P4 with Australia, Peru, and Vietnam. Meanwhile, Malaysia was approved to participate in the TPP negotiations since October 2010; Canada and Mexico joined in December 2012. The current 11 TPP negotiating countries aim to reach the framework agreement until the APEC summit that will be held in October 2013 in Bali, Indonesia, and the final agreement until the end of 2013. The TPP, as a part of the Free Trade Area of the Asia-Pacific (FTAAP), has been known as an open and multilateral FTA that aims to be a comprehensive and a highly liberalized agree-ment among the 21 APEC countries.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".