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Record W1596958810

International environmental law and policy

2002· book· en· W1596958810 on OpenAlexaboutno aff
David Hunter, James Salzman, Durwood Zaelke

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental lawCasebookDiplomacyTreatyPolitical scienceInternational lawMultidisciplinary approachEnvironmental governanceSoft lawCorporate governanceState (computer science)Law and economicsPublic administrationLawEconomicsManagementComputer science
DOInot available

Abstract

fetched live from OpenAlex

This comprehensive, multidisciplinary casebook analyzes all aspects of international environmental law and policy, including the major environmental treaty regimes, customary law principles and the development and evolution of soft law norms. It has been widely adopted in the field for over two decades. Written in a user-friendly fashion with problem exercises and a Teacher's Manual, it emphasizes the dynamic nature of the law-making process, including global environmental diplomacy and the critical role of non-state actors, including scientists, NGOs, and business. Getting to the heart of pressing environmental challenges, it explains not just the law but also the relevant politics, economics, and science.This sixth edition of the book reflects major new developments such as the development of rules to implement the Paris Agreement, the evolution of the Montreal Protocol ozone treaty into an explicit climate treaty, the emergence of successful human-rights litigation to address climate change, the withdrawal of Japan from the Whaling Convention, the U.S.-Mexico-Canada Trade Agreement, and the increasing concern over plastic pollution, to name just a few.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.048
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0030.005
Scholarly communication0.0100.006
Open science0.0010.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0480.014

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 head0.011
GPT teacher head0.266
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations205
Published2002
Admission routes1
Has abstractyes

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