Iura Novit Curia in International Arbitration
Bibliographic record
Abstract
Iura Novit Curia in International Arbitration addresses a question that has attracted the attention of both scholars and practitioners for some time, namely that of whether arbitral tribunals may develop their own legal reasoning independently of the agreement and pleadings of the parties, something that may be looked upon as an oxymoron, given that arbitration itself is considered to be nothing but the result of manifestations of party autonomy – at least according to mainstream understanding of arbitration. The national reports included in this book, all drafted by distinguished academics and practitioners, are based on a questionnaire that can be found following this collection. These reports represent 15 major jurisdictions: Argentina, Austria, Brazil, Canada, Denmark, England, France, Germany, Hong Kong, Russia, Singapore, Spain, Sweden, Switzerland, and the USA. The book also includes a general report, as well as a chapter by Friedrich Rosenfeld assessing how the principle iura novit curia is dealt with in international law. Iura Novit Curia in International Arbitration is required reading for all parties involved in the international arbitration process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".