Release of a Detained Warship and Its Crew through Provisional Measures: A Comparative Analysis of the ARA Libertad and Ukraine v. Russia Cases
Bibliographic record
Abstract
The determination of whether to release a detained foreign warship and its crew is a crucial issue in law and in practice. This article examines the issue of the release of a detained foreign warship and its crewmembers through provisional measures by analyzing the ARA Libertad and Ukraine v. Russia cases. Specifically three issues must be examined. The first issue concerns the interpretation of military activities under Article 298(1)(b) of the U.N. Convention on the Law of the Sea (UNCLOS). On this issue, this article highlights that a threshold for deciding the preponderance of military or law enforcement elements is of critical importance when there are mixed military and law enforcement activities. The second issue concerns the immunity of a detained ship under UNCLOS Articles 29, 32, 95, and 96. In this regard, this article argues that UNCLOS Article 32 may apply to internal waters, even though opinions of the members of ITLOS were divided on this matter. The third issue relates to the question of urgency in ordering provisional measures concerning the release of a detained ship and its crew. Here, this article argues that when identifying the existence of a real and imminent risk, there is a need to consider three temporal elements: the alleged breach of the rights of the applicant State (the past), the existence of ongoing risk (the present), and the possibility of repetition or continuity of the risk (the future).
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".