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Record W2528924350 · doi:10.1080/13563467.2017.1240669

The political economy of decarbonisation: from green energy ‘race’ to green ‘division of labour’

2016· article· en· W2528924350 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNew Political Economy · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRenewable energyInterdependenceGreen economyFraming (construction)Green growthEconomicsEconomic systemPoliticsBusinessMarket economyPolitical economyEconomyPolitical scienceSociologyEngineeringSustainable developmentSocial science

Abstract

fetched live from OpenAlex

This paper aims to provide an addendum to the rapidly growing concept of a global ‘green energy race’ between major states. It argues that although this framing has been useful in underscoring important dynamics in the process of decarbonisation, its narrow focus on installed capacity obscures a much broader and more complex process at play. In particular, it overlooks the critical role played by states aggressively investing in R&D and export manufacturing in the renewable energy sector. The paper thus supplements the concept of a green ‘energy race’ with that of a green ‘global division of labour’, which sees the process of decarbonisation not exclusively as an effort by individual states to install renewables domestically, but rather as a collective and interdependent process by dozens of states, all striving in different ways to promote capital accumulation on their soil. The paper provides an overview of data covering innovation, manufacturing and deployment in the clean energy sector, and offers a theoretical analysis of the trends observed.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.232
Teacher spread0.219 · 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