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Record W2106401393 · doi:10.1162/glep_a_00228

On the Design of an International Governance Framework for Geoengineering

2014· article· en· W2106401393 on OpenAlexaboutno aff
Ian D. Lloyd, Michael Oppenheimer

Bibliographic record

VenueGlobal Environmental Politics · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Geoengineering
Canadian institutionsnot available
Fundersnot available
KeywordsLegalizationCorporate governanceAppropriationGeoengineeringClimate governanceTreatyInternational regimePolitical scienceClimate changeIncentiveLaw and economicsPolitical economyBusinessEconomicsLawEcology

Abstract

fetched live from OpenAlex

This paper explores the governance options surrounding geoengineering—the deliberate, large-scale manipulation of the Earth's climate system to counteract climate change. The authors focus solely on methods that affect the incoming solar radiation to the atmosphere, referred to as solar radiation management (SRM). They examine whether an international governance framework for SRM is needed, how it should be designed, and whether it is feasible. The authors propose a governance regime that initially has small membership and weak legalization, and is flexible in that future institutional reforms allow for broader membership and deeper commitments. The article provides supporting evidence for key aspects of the regime through past international treaties in arms control and environmental protection, including the Antarctica, Outer Space, and Montreal Protocol treaty regimes. For these cases, acting early and treating the respective problems as part of the “regulation of unexplored territory” produced more effective outcomes than the “national appropriation” approach that characterizes arms control.

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)0.026
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0070.022
Scholarly communication0.0120.015
Open science0.0020.008
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0080.001

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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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 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

Citations79
Published2014
Admission routes1
Has abstractyes

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