Reducing the Impact of Mistranslated Testimony in International Arbitral Hearings
Bibliographic record
Abstract
Issues of language and translation do receive attention from the international arbitration community, but most of that attention has been excessively narrow. The majority of international arbitration legislation, rules of procedure, and practice guides only go so far as to recommend ways to avoid the need for translation as much as possible. Little effort is given to improving translation when it is needed. This is a mistake and a missed opportunity. Translation is often inevitable in international arbitrations, and translation inevitably changes meaning. Mistranslation is therefore not just a technical problem, but one with serious legal consequences. This article focuses on the stage of arbitral proceedings where mistranslations with serious legal consequences are most likely to arise: live interpretation of witness testimony. It seeks to educate international arbitration practitioners about the ways — avoidable and unavoidable — that translation changes meaning, describes the inadequacy of current law and practice to deal with these linguistic realities, and presents a series of practical steps that arbitrators and counsel can take. Adopting these recommendations will help to reduce the incidence of mistranslations and to blunt the legal impact of those mistranslations that do arise.
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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 | 0.069 | 0.279 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".