Bibliographic record
Abstract
Abstract This chapter describes the most prominent Brazilian arbitration institutions. These include the Center of Arbitration and Mediation of the Chamber of Commerce Brazil-Canada (CAM-CCBC); the Chamber of Conciliation, Mediation and Arbitration (CIESP/FIESP Chamber); the Chamber of Arbitration of the Market (CAM do Mercado); the Chamber of Mediation and Entrepreneurial Arbitration—Brazil (CAMARB); the Center of Arbitration and Mediation (AMCHAM Center); the Brazilian Center of Mediation and Arbitration (CBMA Center); and the FGV Chamber of Mediation and Arbitration (FGV Chamber). The chapter analyses the limitation of the chambers as well as the aspects that may help in the choice of one of them when executing the arbitration agreement. It also looks at the solutions that have been offered by these chambers for complex matters, such as (i) multiparty arbitration; (ii) connection between procedures; (iii) diversity in the formation of the arbitral tribunals; (iv) and disclosure of information regarding the procedure and the arbitral award. It must be mentioned that, in 2017, the International Chamber of Commerce (ICC), by means of its Court of Arbitration, opened an office in São Paulo to manage arbitration procedures that have minimum contact with parties or with the Brazilian headquarters.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".