Singapore Convention Defences Based On Mediator's Misconduct: Articles 5.1(e) & (f)
Bibliographic record
Abstract
At the February 2019 ICC Mediation Competition in Paris, Damien Cote from Canada and David Lewis from New York moderated a "debate" on the Singapore Convention. One of the panellists launched into a full-scale attack on the Convention, in which he dismissed it on the basis that the "whole document resembled the New York Convention and was redolent of arbitration rather than mediation." The speaker focused on Article 5 and the Grounds for Refusing Relief, and he was particularly critical of Articles 5.1(e) and (f). He expressed his view that these articles were apposite to the setting aside of an arbitral award and therefore had no relevance to mediation and ought not to be a basis for a challenge to a consensual settlement of an international commercial dispute.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.043 | 0.019 |
| Insufficient payload (model declined to judge) | 0.016 | 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".