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Record W2790202896 · doi:10.1002/wcc.506

Political economies of climate change

2018· article· en· W2790202896 on OpenAlexaff
Matthew Paterson, Xavier P‐Laberge

Bibliographic record

VenueWiley Interdisciplinary Reviews Climate Change · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEmbeddednessPolitical economy of climate changeCapitalismClimate changeCorporate governancePoliticsPolitical economyEconomicsEconomic systemClimate governanceGreenhouse gasPolitical scienceSociologySocial scienceEcology

Abstract

fetched live from OpenAlex

Political economy approaches across the social sciences provide powerful explanations for important dynamics within the global response to climate change. This article discusses in particular how they provide explanations of the social origins of greenhouse gas emissions, the dominant policy and governance responses to climate change, recurrent political conflicts over these responses, and the patterns of bargaining between states, businesses, and other actors. Underlying these dynamics are a set of contradictions or tensions between the character of capitalism as a social system and the demands of decarbonizing the global economy, specifically: between the imperative for growth that constrains and shapes responses; concerning the power of large transnational businesses and other incumbent interests to block responses; and over the embeddedness of carbon emissions in daily life. The article explores the implications of these contradictions as well as some of the important theoretical debates about the limits of political economy approaches. This article is categorized under: Policy and Governance > Multilevel and Transnational Climate Change Governance Climate Economics > Economics and Climate Change

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)0.003
metaresearch head score (Gemma)0.006
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: Review · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.008
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.213
GPT teacher head0.349
Teacher spread0.137 · 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
GenreReview

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

Citations94
Published2018
Admission routes1
Has abstractyes

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