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Record W2742247191 · doi:10.5539/enrr.v7n3p36

The Co-Evolution of Institutional Logics and Boundary Spanning in Sustainability Transitions: the Case of Urban Stormwater Management in Melbourne, Australia

2017· article· en· W2742247191 on OpenAlexvenueno aff
Christoph Brodnik, Rebekah Ruth Brown

Bibliographic record

VenueEnvironment and Natural Resources Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsBoundary spanningAgency (philosophy)SustainabilityBoundary (topology)Economic geographyStructure and agencyBusinessEnvironmental planningSociologyGeographyKnowledge managementComputer scienceEcologySocial science

Abstract

fetched live from OpenAlex

Institutions and actors both play a critical role in sustainability transitions, yet they have never been investigated together. This study combines both dimensions, systematically applying an institutional logics and boundary spanning lens to examine the interplay between structure and agency over the course of a particular system innovation: the case of urban stormwater management in Melbourne, Australia. Looking through this dual lens reveals the socio-institutional patterns and mechanisms that determine inertia and practice change in the urban stormwater management system. We found that structure and agency co-evolve; when their relationship changes, the dynamics between boundary spanning and institutional logics change too – as do the corresponding niche and regime developments. Our findings suggest that even radical innovations can be successfully institutionalized when using certain forms of boundary spanning at specific phases of a system innovation.

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)0.002
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 score0.028
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
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 head0.040
GPT teacher head0.337
Teacher spread0.297 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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