‘국제형사재판소에 관한 로마 규정(Rome Statute of the International Criminal Court)’의 국내적 이행 - 현상과 과제
Bibliographic record
Abstract
The success of the International Criminal Court depends not only on widespread ratification of the ‘Rome Statute of the International Criminal Court', but also on states parties’ compliance with obligations under the ‘Rome Statute’. This requires, for almost every state, some change in national law in accordance with existing laws and proceedings in a given legal system. This paper reviews the provisions of the ‘Rome Statute’ and analyses the national legislation on its implementation; it involves a comparative analysis of implementation strategies adopted by the United Kingdom, Canada, Germany, Switzerland and the Netherlands. Approaches adopted by states with regards to specific issues of implementation will also come into focus, followed by discussions on implications of the 'Rome Statute' for the implementation in Korea. In 2007, the Republic of Korea enacted the ‘Law on the Prosecution and Punishment of the Crime of the Rome Statute of the International Crimi nal Court’, which is the implementation of the ‘Rome Statute’. The Law lists and criminalizes all core crimes that are within the jurisdiction of the International Criminal Court; and gives the Korean government a statutory basis for transferring suspects to the Court and makes it possible to furnish the Court with legal assistance. This paper focuses on several specific rules of the Law and research questions that the study is set out to answer. In conclusion, it recommends and argues the need for a comprehensive domestic implementation strategy of the ‘Rome Statute’ in Korea.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".