The <i>South China Sea</i> arbitration: Environmental obligations under the Law of the Sea Convention
Bibliographic record
Abstract
This case note analyses the marine environmental protection issues that arose in the 2016 South China Sea arbitration. Given that the South China Sea includes highly productive fisheries and extensive coral reef ecosystems, the alleged breach of environmental obligations under the United Nations (UN) Convention on the Law of the Sea was important in this arbitration. The Arbitral Tribunal examined three obligations concerning marine environmental protection under the UN Convention on the Law of the Sea: the obligation of due diligence; the obligation to conduct an environmental impact assessment; and the obligation to cooperate. The Tribunal's arbitral award contributes to the clarification of the interpretation of relevant provisions concerning marine environmental protection under the Convention. Furthermore, a remarkable feature of the arbitration was that the Tribunal appointed experts to have an independent opinion with regard to environmental damages arising from China's activities in the South China Sea. The use of experts in the South China Sea arbitration is worth noting, since scientific evidence is of particular importance in the settlement of international environmental disputes.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| 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".