Understanding the relationship between Pacific Alliance and the mega-regional agreements in Asia-Pacific: what we learned from the GTAP simulation
Bibliographic record
Abstract
The Asia-Pacific region is the epicenter for the emergence of a series of mega-regional agreements such as the Trans-Pacific Agreement (TPP), the Regional Comprehensive Economic Partnership (RCEP), and the Pacific Alliance (PA)established in 2011 among Chile, Colombia, Mexico and Peru. However, since early 2017 the region has experience risingprotectionism, as seen in the decision of the United States to withdraw fromTPP, sending shockwaves across the region. The PA has decided to continue with its process, recently launching negotiations with four associated members (Australia, Canada, New Zealand and Singapore). This is the context of structural changes and uncertainty that the Pacific Alliance must now face. This raises questions such as: What role should the Pacific Alliance play in the new regional architecture intheAsia Pacific? How should the Pacific Alliance prepare to maintain its relevance in a context of mega-regional agreements that include several of its members? Based on these and other questions, the authors formulated a total of six scenarios that describe possible interactions among the Pacific Alliances and the other mega-regional agreements. These scenarios were tested using GTAP to understand which of them would have a more positive impact on regional exports via both tariff reduction and trade facilitation measures. The results from these scenarios, suggest that the one that would have the greatesteffect on exports would be the Integration of the Pacific Alliance economies to the Regional Comprehensive Economic Partnership (RCEP), followed by the Comprehensive and Progressive Trans-Pacific Partnership with the participation of Colombia and China (CPTPP 13). These scenarios offer significant increases in the aggregate exports of the group, both in the face of the tariff reduction and in response totrade facilitation reforms.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".