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Record W2802825403 · doi:10.3390/en11051198

Technology and Instrument Constituencies as Agents of Innovation: Sustainability Transitions and the Governance of Urban Transport

2018· article· en· W2802825403 on OpenAlexaff
Nihit Goyal, Michael Howlett

Bibliographic record

VenueEnergies · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsSimon Fraser University
FundersLee Kuan Yew School of Public Policy, National University of SingaporeNational University of Singapore
KeywordsConceptualizationAgency (philosophy)Corporate governanceSustainabilityMobilitiesBusinessPerspective (graphical)Economic systemSociologyPolitical scienceEconomicsSocial science

Abstract

fetched live from OpenAlex

Sustainable urban transport is a complex challenge requiring innovation in technologies, culture, and policies. Given the systemic nature of the issues involved, numerous studies have applied the transitions approach to urban transport. However, relatively weak conceptualization of agency in the transitions literature limits the usefulness of this approach for the governance of urban transport. The objective of this study is to contribute to the conceptualization of agency in the multilevel perspective to sustainability transitions. We propose that two types of actors exercise agency to foster innovation: technology constituencies, who promote the adoption of specific technologies by citizens, businesses, or governments; and instrument constituencies, who promote the adoption of specific policy instruments. In focusing predominantly on technological innovation, the transitions literature has generally juxtaposed these constituencies or considered them to be the same. We posit that the two constitute distinct, albeit possibly overlapping, actors and that their relationship(s) help better understand and explain how transitions evolve. We discuss the implications of this distinction for the governance of urban transport and argue that the presence of instrument and technology constituencies, and their relationship(s), should be examined empirically in future research.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score0.997

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 studies0.0000.006
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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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.

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

Citations25
Published2018
Admission routes1
Has abstractyes

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