Some Reflections on the Resolution of State-to-State Disputes in International Waters Governance Agreements
Bibliographic record
Abstract
This paper reviews various dispute resolution mechanisms that have, or may have, application in international waters governance agreements. International waters are water resources that are shared by two or more states. They include international freshwater, international groundwater and international Large Marine Ecosystem (LMEs) situations. There are a number of possible types of dispute resolution mechanisms in international waters governance agreements. They include: (1) international courts, such as the International Court of Justice; (2) standing regional courts and tribunals, such as the Southern African Development Community (SADC) Tribunal; and (3) ad hoc arbitration, such as arbitrations administered by the Permanent Court of Arbitration. While no one type of dispute resolution mechanism is suitable for all states in all situations, some of the objectives that may be sought in dispute resolution in international waters governance agreements arguably include: (1) obtaining an effective remedy; (2) obtaining a correct result and (3) maximizing the efficiency, in terms of cost and/or timing, of the decision-making process. Having an efficacious dispute resolution enforcement mechanism in an international waters governance agreement may help ensure that a state can obtain an effective remedy even when an opposing state fails to voluntarily comply with a decision in a timely manner. Providing for an enforcement mechanism in an international waters governance agreement may also help encourage voluntary compliance as it may move states to consider the costs of non-compliance.
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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 | 0.059 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.015 | 0.075 |
| Scholarly communication | 0.032 | 0.044 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.029 | 0.033 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".