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Record W4389389546 · doi:10.20935/acadenvsci6141

Beyond domain-led conceptualizations of urban zero-carbon transitions

2023· article· en· W4389389546 on OpenAlexaffabout
Andrew Sudmant, Matt Tierney, Andy Gouldson, Joule Bergerson

Bibliographic record

VenueAcademia Environmental Sciences and Sustainability · 2023
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsUniversity of Calgary
FundersEconomic and Social Research CouncilCentre for Climate Change Economics and Policy, University of LeedsForeign and Commonwealth Office
KeywordsZero (linguistics)Domain (mathematical analysis)Carbon fibersEnvironmental scienceComputer scienceMathematicsPhilosophyLinguisticsAlgorithmMathematical analysis

Abstract

fetched live from OpenAlex

Rapid, systemic change is needed to achieve zero emissions, but there is uncertainty about how or where to intervene in urban systems. Drawing on the work of Donella Meadows, we apply a Leverage Points Perspective to identify and characterize points of system-level intervention that emerge from a study of climate action in Calgary, Canada, which was unique in applying a mixed set of academic approaches. Reflecting on Meadows’ and other frameworks for conceptualizing complex systems change, we discuss the challenge of conceptualizing change, a task of unique urgency in the context of the climate emergency. Too frequently, we argue, approaches focus attention on specific modes or forms of action seen to have the greatest opportunity for affecting change in place of the complex chains of actors, objects, and processes that collectively are the key to a deep and sustaining transition. We conclude by exploring how the insights of the Leverage Points Perspective and other approaches can be brought together to inform practical action, and by examining how related theoretical work on provisioning systems and applied work on urban Climate Commissions may be drawn on to advance understanding of how to deliver urban systems change.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.034
GPT teacher head0.329
Teacher spread0.296 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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