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Record W2793216887 · doi:10.1007/s11077-018-9314-8

The politics of decarbonization and the catalytic impact of subnational climate experiments

2018· article· en· W2793216887 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePolicy Sciences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversity of Toronto
FundersSolar Energy Technologies OfficeSocial Sciences and Humanities Research Council of Canada
KeywordsClimate governanceCorporate governancePoliticsContext (archaeology)Normalization (sociology)Political scienceGlobal governanceCollective actionEconomic systemClimate changeTransformative learningState (computer science)Political economyEconomicsSociologyEcologyManagementLaw

Abstract

fetched live from OpenAlex

The Paris Agreement of 2015 marks a formal shift in global climate change governance from an international legal regime that distributes state commitments to solve a collective action problem to a catalytic mechanism to promote and facilitate transformative pathways to decarbonization. It does so through a system of nationally determined contributions, monitoring and ratcheting up of commitments, and recognition that the practice of climate governance already involved an array of actors and institutions at multiple scales. In this article, we develop a framework that focuses on the politics of decarbonization to explore policy pathways and mechanisms that can disrupt carbon lock-in through these diverse, decentralized responses. It identifies political mechanisms-normalization, capacity building, and coalition building-that contribute to the scaling and entrenchment of discrete decarbonization initiatives within or across jurisdictions, markets, and practices. The role for subnational (municipal, state/provincial) climate governance experiments in this new context is especially profound. Drawing on such cases, we illustrate the framework, demonstrate its utility, and show how its political analysis can provide insight into the relationship between climate governance experiments and the formal global response as well as the broader challenge of decarbonization.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
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 head0.022
GPT teacher head0.336
Teacher spread0.313 · 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