Recognition and enforcement of international commercial arbitral awards in Latin America : law, practice and leading cases
Bibliographic record
Abstract
Recognition and Enforcement of International Commercial Arbitral Awards in Latin America: Law, Practice and Leading Cases Edited by Omar Garcia-Bolivar and Hernando Otero Dedication Table of Cases Introduction by Omar Garcia-Bolivar and Hernando Otero 1. Argentina by Julio Cesar Rivera 2. Bolivia by Fernando Aguirre B. 3. Brazil by Nadia de Araujo and Ricardo Ramalho Almeida 4. Chile by Gonzalo Biggs 5. Colombia by Rafael Bernal and Hernando Otero 6. Costa Rica by Roy Herrera 7. Dominican Republic by Lorena Perez McGill 8. Ecuador by Alvaro Galindo and Francisco Endara 9. El Salvador by Roberto Jose Tercero 10. Guatemala by Alvaro Castellanos Howell 11. Honduras by Fanny Rodriguez and Mario Aguero 12. Mexico by Claudia Frutos-Peterson and Antonio Riva Palacio 13. Nicaragua by Fernando Medina Montiel and Jose Rene Cruz Orue 14. Panama by Katherine Gonzalez Arrocha and Adrian Martinez Benoit 15. Paraguay by Diego Zavala 16. Peru by Carlos Paitanand Danny Quiroga 17. Uruguay by Leonardo Melos 18. Venezuela by Diana Droulers, Emilio Garcia-Bolivar and Adriana Vaamonde M. Contributors Index
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".