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Record W2159393770 · doi:10.1068/c3105ed

Novel Multisector Networks and Entrepreneurship in Urban Climate Governance

2013· article· en· W2159393770 on OpenAlexaff
Heike Schroeder, Sarah Burch, Steve Rayner

Bibliographic record

VenueEnvironment and Planning C Government and Policy · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTransformative learningVariety (cybernetics)Corporate governanceSustainabilityContext (archaeology)Theme (computing)EntrepreneurshipClimate governanceNetwork governanceClimate changeSociologyPolitical scienceState (computer science)Economic geographyKnowledge managementGeographyManagementEcologyEconomicsComputer science

Abstract

fetched live from OpenAlex

The papers in this theme issue seek to advance our understanding of the roles of networks and partnerships in the multilevel governance of climate change and related issues in the urban context. In particular, the papers examine the roles of nontraditional actors and apply emerging theoretical approaches such as sustainability transitions theory to gain a greater understanding of the variety of approaches being employed around the world, as well as the transformative potential of these approaches. We discuss the role of the state relative to the roles of local leadership, knowledge systems, and community-wide collaborative engagement in bringing about sustainability 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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.917

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 studies0.0000.000
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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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 designObservational
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
Published2013
Admission routes1
Has abstractyes

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