比較的レンズをとおした仲裁の考察：一般的原則と具体的争点 (Looking at Arbitration through A Comparative Lens: General Principles and Specific Issues)
Bibliographic record
Abstract
Japanese Abstract: 本稿は比較的観点から仲裁の様々な争点を探求する。コモンロー系とシヴィルロー系の法域はもちろんのこと東洋と西洋の法域も比較対照する。東洋の法域については中国、香港、シンガポール、日本および韓国などの重要なアジア法域に言及していく。西洋の法域についてはフランス、ドイツおよびスイス、ならびにイギリス、アメリカ、オーストラリアおよびカナダといった重要なコモンロー系の法域に触れていく。本稿で論じられるように、これらの法域では、三つの原動力が仲裁に関する争点に影響を与えている。第１に西洋と東洋の間の文化の相違である。第２にコモンロー系とシヴィルロー系の法制度に根付いている実務の違いである。第３に紛争解決制度における伝統であり、それは調停の採用への態度を擁している。本稿の各章における比較的分析は、上記で特定した原動力が仲裁の様々な争点に関する相違にどのように寄与しているのかについて包括的な考察を可能とする。 English Abstract: This article explores various issues of arbitration from a comparative perspective. It compares and contrasts jurisdictions in the East and the West, as well as those of a common law and civil law legal system. Eastern jurisdictions refer to prominent Asian jurisdictions such as China, Hong Kong, Singapore, Japan and Korea. The West refers to major Continental European jurisdictions such as France, Germany and Switzerland, and prominent common law jurisdictions such as the UK, the US, Australia and Canada. Three driving forces, as argued in this chapter, influence issues of arbitration in these jurisdictions. The first is the cultural differences between the West and the East. The second is the differences in the practice embedded in common law and civil law legal systems. The third is the traditions within the dispute resolution system, which includes the attitudes towards adopting mediation. The comparative analysis in each part of this article allows for a comprehensive examination of how the driving forces identified above contribute to the differences on various issues of arbitration.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.066 |
| Scholarly communication | 0.016 | 0.028 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".