The Elgar Companion to the Hague Conference on Private International Law
Bibliographic record
Abstract
The publication presented here gathers some 35 contributions, drafted by even more authors, who are practitioners and/or academics and hold positions in various places in the world, including Mexico, Singapore, Italy, France, Belgium, Australia, India, Russia, Japan or Canada. As the title shows, the main objective of this undertaking is to take stock of the evolutions, achievements, and challenges of the Hague Conference on Private International Law (HCCH). In this respect, the book is the first of its kind as it describes and assesses the 125 years of work of the HCCH. In doing so, it also performs two other noteworthy functions. On the one hand, it constitutes a useful legal resource for practitioners and students, as several contributions establish the state of the art about a given, sometimes new, topic of private international law (see e.g., ch. 30 on commercial arbitration), within ten to fifteen pages. On the other hand, this publication identifies the main issues that require the attention of the HCCH, and, in doing that, more generally points out the topics where positive private international law seems absent or out-of-date (see e.g., ch. 33 on the notion of network).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".