The 2024 ITLOS COSIS Advisory Opinion: Delivering Climate Justice for Small Island States
Bibliographic record
Abstract
Abstract for Scopus Indexing: In May 2024, the International Tribunal on the Law of the Seas ( ITLOS ) issued a historic advisory opinion on the obligations of States regarding climate change under the United Nations Convention on the Law of the Seas. This advisory opinion was issued in response to a request submitted by the Commission on Small Island States on Climate Change and International Law. Significantly, the Tribunal stated that: a) States must interpret their obligations under the Convention in light of the best available science; b) greenhouse gas ( GHG ) emissions constitute pollution of the marine environment; and c) they must take all necessary measures to reduce, prevent and control GHG emissions. ITLOS also noted that developed States have an obligation under the Convention to assist developing States in mitigating the effects of climate change. This advisory opinion marks an important first step in the fight for climate justice for small island States. Forthcoming are advisory opinions concerning climate change from the International Court of Justice ( ICJ ) and the Inter-American Court of Human Rights ( IACHR ). In this article, we explore the jurisdictional arguments some participants raised, the significance of the Tribunal’s opinion, and we contextualize the opinion in light of broader efforts towards climate justice.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.076 | 0.025 |
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".