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Record W3142859212 · doi:10.1177/00207020221097998

Power Shift: The Global Political Economy of Energy Transitions

2022· article· en· W3142859212 on OpenAlexaff
Joshua K. McEvoy

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2022
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy Security and Policy
Canadian institutionsQueen's University
Fundersnot available
KeywordsPower (physics)Economic systemPolitical economyPolitical scienceEconomicsPhysics

Abstract

fetched live from OpenAlex

Energy transitions are fundamental to achieving a zero-carbon economy. This book explains the urgently needed transition in energy systems from the perspective of the global political economy. It develops an historical, global, political and ecological account of key features of energy transitions: from their production and financing, to how they are governed and mobilised. Informed by direct engagement in projects of energy transition, the book provides an accessible account of the real-world dilemmas in accelerating transitions to a low carbon economy. As well as changes to technology, markets, institutions and behaviours, Power Shift shows that shifts in power relations between and within countries, and across social groups and political actors, are required if the world is to move onto a more sustainable path. Using contemporary and historical case studies to explore energy transitions, it will be of interest to students and researchers across disciplines, policymakers and activists.

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.020
Scholarly communication0.0090.010
Open science0.0010.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0100.001

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 head0.006
GPT teacher head0.258
Teacher spread0.253 · 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

Citations91
Published2022
Admission routes1
Has abstractyes

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