Iura Novit Arbiter revisited: towards a harmonized approach?
Bibliographic record
Abstract
In French-language Decision 4A_538/2012 of 17 January 2013, the Swiss Federal Supreme Court provided clarification regarding the arbitrators’ duty to inform parties before rendering an award based on an unexpected legal reasoning.1 In this case, the appellant argued that a piece of evidence had been adduced only to establish certain facts, whereas the tribunal had diverted the evidence from its goal, deducing from it another fact. The Federal Supreme Court held that, according to the case law, the tribunal may be exceptionally under a duty to advise the parties when it considers basing its decision on a provision or a legal consideration that was not raised during the proceedings and the pertinence of which the parties could not guess, but that this jurisprudence does not concern the establishment of facts. Based on this recent decision, this article aims to analyse the origin and current practice of iura novit curia in relation to the application of foreign law in state litigation as well as in arbitration. It also seeks to examine reasons and limits for the principle, to propose a reformulation of the principle according to the results of the analysis, as well as to offer relevant recommendations.
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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 | 0.049 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.032 | 0.013 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.029 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".