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Record W4392287653 · doi:10.1111/1467-9477.12267

Understanding institutional layers and modes of change for energy transitions: Analysis of Norway's electricity sector reforms

2024· article· en· W4392287653 on OpenAlexafffund
Minika Ekanem, Bram Noble, Greg Poelzer, Hans‐Kristian Hernes

Bibliographic record

VenueScandinavian Political Studies · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversity of Saskatchewan
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRestructuringElectricityLayeringHierarchyInstitutional changeEconomic systemEnergy transitionTransition (genetics)BusinessEnergy (signal processing)Scale (ratio)Energy sectorInstitutional analysisIndustrial organizationPolitical scienceEconomicsMarket economySociologyPublic administrationEngineeringGeography

Abstract

fetched live from OpenAlex

Abstract Institutions have significant implications for whether and how energy systems restructure, evolve, and successfully transition. Yet, literature analyzing energy sector reforms often approach transitions from economic or technical perspectives, with much less attention to the underlying roles and influences of institutions. This paper explores the roles and influence of institutions on the speed, direction, timing, and sequence of energy transitions. A conceptual framework integrating the hierarchy of institutions with an historical institutionalist approach is developed and applied to explore transitions in Norway's electricity sector as a case study. Results show that conversion followed by layering emerge as the dominant modes of institutional change in Norway's electricity sector reform, illustrating the importance of alignment between institutions in creating the conditions for large‐scale energy transitions and the importance of boundaries to maintain alignment between levels of institutions. Governments can minimize potential gaps between transition intentions and outcomes through effective conversion and layering of institutional arrangements, but layering challenges emerge when institutional change introduces new actors or energy arenas to existing policy paradigms.

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 categoriesnone
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 score0.268
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

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

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 head0.204
GPT teacher head0.328
Teacher spread0.123 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations3
Published2024
Admission routes2
Has abstractyes

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