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International Environmental Law and Community Interests: Procedural Aspects

2016· article· en· W2467431718 on OpenAlexaff
Jutta Brunnée

Bibliographic record

VenueTSpace · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEnvironmental lawPolitical scienceLawLaw and economicsSociology

Abstract

fetched live from OpenAlex

Strong procedural elements are indispensable for international environmental law’s capacity to serve community interests. Procedural obligations can strengthen the rule concerning the prevention of environmental harm and flesh out its due diligence standard. Procedural obligations can also serve useful purposes when states, or judges, are reluctant to entertain substantive arguments, or find it difficult to establish that environmental harm has been caused. Violations of procedural obligations are more easily established and states can sometimes be prompted to correct harmful conduct or to take more effective preventive measures. Unfortunately, the operation of procedural rules is constrained by the dearth of practice and the continued struggle to define the substance of community obligations and the legal effects of erga omnes norms. Treaty-based approaches have proven better suited to accommodating community concerns, perhaps because they place such strong emphasis on procedural elements and employ increasingly diverse formal lawmaking and informal standard-setting approaches.

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)0.011
metaresearch head score (Gemma)0.015
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: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0070.040
Scholarly communication0.0170.013
Open science0.0020.009
Research integrity0.0130.013
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.012
GPT teacher head0.273
Teacher spread0.261 · 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
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

Citations7
Published2016
Admission routes1
Has abstractyes

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