Bibliographic record
Abstract
This chapter intends to provide a comprehensive overview of the allocation of cases based on geography in civil and common law jurisdictions. Given the impracticality of conducting an exhaustive study of every jurisdiction, the chapter will focus on illustrative jurisdictions such as the United States, England and Wales, Canada, Australia, Singapore, India, South Africa, Mainland China, Taiwan, Hong Kong, Macau, France, Germany, Norway, Poland, Estonia, Italy, Russia, Belgium, Egypt, Algeria, Tunisia, Dubai, Iran, Turkey, Brazil, Argentina, Colombia, Venezuela, Costa Rica, Cuba, Mexico, Japan and South Korea. Additionally, the chapter will include supplemental commentary on other jurisdictions that provide interesting contrasts. The chapter will proceed thematically rather than by jurisdiction and will analyze geographic jurisdiction along various dimensions. This chapter proposes that geographical jurisdiction is influenced by three key factors, (1) national sovereignty; (2) the balance of plaintiff and defendant interests; (3) forum interests.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".