Compensation for Oil Pollution Due to Tanker Accidents in the Indonesian Legal System in a Justice Value Perspective
Bibliographic record
Abstract
The sea potentially fulfills the interests of sea transportation; for example, the transportation of tankers. The Indonesian sea is included in the seas with the dense traffic of tankers causing the risk of oil pollution due to tanker accidents. For example, the three cases of oil contamination caused by tanker accidents occurred in the Cilacap Sea which is the largest oil refinery in Indonesia. This study aimed to find the value of justice for oil pollution losses due to tanker accidents considering that Indonesia has ratified the international convention of the civil liability of oil spill by tanker, Convention on Civil Liability 1969, and its amendment of Convention on Civil Liability 1992, along with its supplementary protocol. The international law principles (polluter pays principle, precautionary principle, and strict liability) for oil tanker losses caused by tankers have been applied to the national legal system. There were still overlapping authorities and the conflicts of authorities among the institutes in the period before 2015 before the establishment of the Coordinating Ministry of Marine Affairs. After the periodization of 2015 with the formation of the Coordinating Ministry of Marine Affairs, it is expected to resolve the loss of oil pollution as a result of tanker accidents using the right method of calculating the loss of natural resources, taking into account the willingness to pay and the willingness to accept between the insurance and victims.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".