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Record W2508773097 · doi:10.1017/s2047102516000157

The Global Commons through a Regional Lens: The Arctic Council on Short-Lived Climate Pollutants

2016· article· en· W2508773097 on OpenAlexaboutno aff
Sabaa A. Khan

Bibliographic record

VenueTransnational Environmental Law · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArcticCorporate governanceGlobal commonsClimate changeGlobal warmingEnvironmental sciencePolitical scienceEnvironmental protectionNatural resource economicsEnvironmental planningOceanographyBusinessEconomics

Abstract

fetched live from OpenAlex

Abstract The regulation of short-lived climate pollutants (SLCPs) is widely seen as an important dimension of global atmospheric pollution control and climate change governance. SLCPs emitted outside the Arctic influence the Arctic atmosphere, Arctic communities, and the rate of ice melt. As an intergovernmental forum that brings together three of the world’s major petroleum producers (Russia, the United States, and Canada), the Arctic Council has a pivotal role in reducing the rate of Arctic warming through SLCP mitigation. This article explores the Arctic Council’s approach to SLCP mitigation. It begins by addressing the current status of black carbon and methane in international legal instruments, and goes on to explore the important regime linkages that are set in place through the Arctic Council’s Framework for Action on Enhanced Black Carbon and Methane Emission Reductions. The article suggests that the Arctic Council provides an experimental platform that may catalyze SLCP regulation not only in Arctic jurisdictions but also in Arctic Council observer states, such as China and India. The transnational and inclusive character of the Arctic Council’s constitutional framework and knowledge-generating mechanisms enables new pathways for global action on climate change and air pollution governance.

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 categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score1.000

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 studies0.0030.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.297
Teacher spread0.216 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations12
Published2016
Admission routes1
Has abstractyes

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