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Record W2791084857 · doi:10.1093/global/guy001

Do G20 Leaders need to put on their Own Emergency Oxygen Masks First? A Look at Germany’s G20 Presidency and Climate Policy

2017· article· en· W2791084857 on OpenAlexaff
Céline Bak

Bibliographic record

VenueGlobal Summitry · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsInternational Institute for Sustainable Development
Fundersnot available
KeywordsPresidencyClimate changePolitical scienceClimate governancePolitical economy of climate changeMandateEconomic policyEconomicsPoliticsLaw

Abstract

fetched live from OpenAlex

This article examines climate policy commitments under the German G20 presidency. It concludes that the fracturing of the G20 consensus on climate change resulted in two course changes—one positive and one negative. A ‘near-consensus’ was expressed in the Hamburg Climate and Energy Action Plan for Growth (CEAG). There the G19 maintained climate commitments, including a recognition of the role of sustainable infrastructure for inclusive low-carbon growth. On the negative side of the ledger, the absence of an ongoing mandate for the Financial Stability Board (FSB) to address the impact of climate change on the global financial system is cause for grave concern. To guard against a further fracturing of the consensus needed for structural reforms—such as carbon pricing—G19 leaders and finance ministers must engage citizens, particularly young citizens, on how best to integrate economic, social, and climate policy.

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)0.004
metaresearch head score (Gemma)0.007
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0130.002

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 head0.088
GPT teacher head0.294
Teacher spread0.205 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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