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Record W2766991621 · doi:10.1016/j.erss.2017.09.025

Conceptual and empirical advances in analysing policy mixes for energy transitions

2017· article· en· W2766991621 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

VenueEnergy Research & Social Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsSimon Fraser University
FundersEngineering and Physical Sciences Research Council
KeywordsConceptualizationCredibilityPolicy analysisSustainabilityPolicy studiesEmpirical researchConsistency (knowledge bases)Management scienceCoherence (philosophical gambling strategy)Energy policyFraming (construction)Conceptual frameworkPublic economicsPolitical sciencePublic policyEconomicsSociologyPublic administrationComputer scienceEngineeringSocial scienceEconomic growthRenewable energyEpistemology

Abstract

fetched live from OpenAlex

Energy transitions face multiple barriers, lock-in, path dependencies and resistance to change which require
\nstrategic policy efforts to be overcome. In this regard, it has been increasingly recognised that a multiplicity of instruments – or instrument mixes – are needed to foster low-carbon transitions. In addition, over the past few years a broader conceptualization of policy mixes for sustainability transitions has emerged which we adopt in
\nthis special issue. Such a broader perspective not only examines the interaction of instruments, but also captures corresponding policy strategies with their long-term targets and pays greater attention to the associated policy processes. It also encompasses the analysis of overarching policy mix characteristics such as consistency, coherence or credibility, as well as policy design considerations. Furthermore, it embraces the analysis of actors and institutions involved in developing and implementing such policy mixes. To explicitly consider these further aspects of policy mixes, this special issue includes fifteen papers with different analytical perspectives drawing on a range of social science disciplines, such as environmental economics, innovation studies and policy sciences. It is our hope that the conceptual and empirical advances presented here will stimulate diverse future research and inform policy advice on policy mixes for energy transitions.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.007
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.097
GPT teacher head0.458
Teacher spread0.361 · 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