Bibliographic record
Abstract
FTA bilateral and regional negotiations in Asia have developed quickly in the past decade moving Asia ever closer to an economic union. Unlike Europe with the EU and the 1997 treaty of Rome and the 1993 NAFTA in North American, Asian economic integration does not involve a comprehensive trade treaty, but an accelerating process of building one bilateral agreement on another. For countries in Asia there is negotiation of a China-Japan-Korea agreement, a China-India agreement, a Trans-Pacific Partnership (TPP) agreement, and a Regional Comprehensive Economic Partnership (RCEP). This paper uses a fifteen-country global general equilibrium model with trade costs to numerically calculate Debreu distance measures between the present situation and potential full Asia integration in the form of a trade bloc. Our results reveal that these large Asia economies can be close to full integration if they act timely in agreements through negotiation. All Asia countries will gain from Asia trade bloc arrangements except when the Asia FTA can only eliminate tariffs. These countries' gain will increase as bilateral non-tariff elimination deepens. Larger countries will gain more than small countries. Asia FTA, Asia Union and RCEP will benefit member countries more than ASEAN+3. Global free trade will benefit all countries the most.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.016 |
| 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".