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Record W2256341401 · doi:10.1017/s0069005800008754

Coming in from the Shadow of the Law: The Use of Law by States to Negotiate International Environmental Disputes in Good Faith

2006· article· en· W2256341401 on OpenAlexaffvenue
Cameron J. Hutchison

Bibliographic record

VenueCanadian Yearbook of international Law/Annuaire canadien de droit international · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLawLegitimacyNegotiationPolitical scienceDutyContext (archaeology)Soft lawEnvironmental lawLaw and economicsDispute resolutionShadow (psychology)ObligationInternational lawSociologyPoliticsGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.005
GPT teacher head0.180
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2006
Admission routes2
Has abstractyes

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Same venueCanadian Yearbook of international Law/Annuaire canadien de droit internationalSame topicInternational Maritime Law IssuesFrench-language works237,207