FREE AND OPEN INDO–PACIFIC AS THE NEW BASIS OF JAPANESE TRADE POLICY IN THE ASIA–PACIFIC REGION
Bibliographic record
Abstract
The consequences of the APEC summit held on November 17–18, 2018 are analyzed. It is emphasized that the summit ended with little or no result due to the growing trade and economic contradictions between theUnited StatesandChina, which have the potential to significantly weaken trade and economic ties in the Asia-Pacific region. Under these conditions, after theUSwithdrawal from the Trans-Pacific Partnership (TTP),Japanis increasingly taking the path of bilateral and regional agreements with the countries of the Asia-Pacific Region, including the People's Republic ofChina.Japanconsistently pursues this policy in the framework of the strategy of the Free and Open Indo-Pacific Region proclaimed in 2016. This strategy will allowJapanto insure itself against the possible eventual occurrence of the American-Japanese trade war, taking into account the fact thatJapanhas a growing positive balance in trade with theUnited States.Japanhas all high hopes on the establishment of a Regional Comprehensive Economic Partnership with the participation of 16 countries. At the same time, the APR is increasingly becoming a zone of military rivalry between the states of this region, which is an additional factor complicating the ongoing development of Japan’s trade and economic ties with many APR 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".