Coming in from the Shadow of the Law: The Use of Law by States to Negotiate International Environmental Disputes in Good Faith
Bibliographic record
Abstract
Summary International law increasingly obliges states to negotiate in good faith environmental disputes that arise in connection with the use and protection of shared or common property natural resources — that is, watercourses, fisheries, and migratory species. Articulation of this duty to negotiate in good faith has been vague, and, perhaps as a consequence, disputes have been protracted or have gone unresolved. Part of the problem may be that states do not know how to interpret their competing rights in the resource. This article explores the facilitative potential of international authoritative soft law to good faith negotiation where rights and obligations of resource use and protection are broadly stated and their relationship to one another is unclear. In this context, our understanding of the relevance, sources, and use of law in the negotiation process contributes to whether law functions to facilitate or frustrate dispute resolution. Through discursive interaction undertaken in good faith, states should look to international authoritative soft law to explicate, integrate, and reconcile their legitimate interests within their competing rights and obligations, according to prescribed legitimacy criteria.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.055 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".