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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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.014
Scholarly communication0.0050.003
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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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