Pacific alliance allies and its commercial opportunities with Colombia
Bibliographic record
Abstract
According to the Americas Society / Council of the Americas, the members of the P.A. account \naround 37% of Latin America’s total GDP, 50% of the country’s exports and 45% of foreign \ninvestment. ( Americas Society / Council of the Americas, 2018). Due to de dynamism of this \nalliance the world has started to interest in it. Now, the P.A. has about 52 observer countries who \ncan participate of the meetings of the Alliance and can also apply to become a full member of the \nP.A. if they have trade deals in place with at least half of the coalition’s full members. They are, \nin America: Argentina, Canada, Costa Rica, Ecuador, El Salvador, United States, Guatemala, \nHaiti, Honduras, Panama, Paraguay, Dominican Republic, Trinidad and Tobago, and Uruguay. In \nAfrica: Egypt and Morocco. In Asia: China, Korea, India, Indonesia, Israel, Japan, Singapore, and \nThailand. In Europe: Germany, Austria, Belgium, Croatia, Denmark, Slovakia, Slovenia, Spain, \nFinland, France, Georgia, Greece, Hungary, Italy, Lithuania, Norway, Netherlands, Poland, \nPortugal, United Kingdom, Czech Republic, Romania, Sweden, Switzerland, Turkey, and Ukraine. \nAnd last in Oceania: Australia and New Zeeland. Now, there are already two countries in the \nprocess to access as full member to the P.A., Costa Rica and Panama. (International Centre for \nTrade and Sustainable Development, 2018)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.058 | 0.005 |
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".