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Record W3139875535

Resources and international climate change policy gridlock

2014· article· zh· W3139875535 on OpenAlexaboutno aff
Pandey

Bibliographic record

Venue中国人口资源与环境:英文版 · 2014
Typearticle
Languagezh
FieldEnergy
TopicGlobal Energy Security and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changePolitical economy of climate changeGridlockNatural resource economicsNatural resourceThreatened speciesGreenhouse gasDevelopment economicsGlobal warmingPolitical scienceEconomicsPoliticsGeographyEcology
DOInot available

Abstract

fetched live from OpenAlex

Few other policy zones are as complex as the issue of climate change.If the more pessimistic projections of climate change doom are correct,then the failure to address the issue is likely to be catastrophic and irreversible.The Inter-governmental Panel on Climate Change has predicted the potential extinction of many species and that the existence of small-island and other vulnerable countries will be threatened if business-as-usual greenhouse gas emissions continue.Climate change is a transboundary problem and requires unprecedented levels of cooperation between states and serious and sustained responses from major emitters.However,the growing demand and consumption of natural resources for continued energy security and cornucopian economic growth have undermined the outcomes of international climate change negotiations.It is argued here that there is a strong connection between the major emitters’positions at United Nations’climate talks,their possessions,dependence and consumption of natural resources,and the continued undermining of international climate change policy for unsustainable growth.This paper assesses the resource politics of the US,China,India,Canada,Russia,and Saudi Arabia and their positions at climate talks to show the link between lack of climate change policy progress and the positions of these main players.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.274
Teacher spread0.252 · 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 teacher head, not a consensus.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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