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Record W4229364341 · doi:10.54175/hsustain1020006

Uncovering the Holistic Pathways to Circular Cities—The Case of Alberta, Canada

2022· article· en· W4229364341 on OpenAlexaboutno aff
Marjan Marjanović, Wendy Wuyts, Julie Marin, Joanna Williams

Bibliographic record

VenueHighlights of Sustainability · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsnot available
FundersVlaamse regeringFonds Wetenschappelijk OnderzoekUniversity College London
KeywordsCircular economyScope (computer science)SustainabilityPerspective (graphical)Regional scienceGeographySociologyEnvironmental planningEconomic geographyComputer scienceEcology

Abstract

fetched live from OpenAlex

The notion of circularity has gained significant attention from governments of many cities across the world. The approaches to circular cities may range from narrower perspectives that see a circular city as the simple sum of circular economy initiatives to those more holistic that aim to integrate the whole urban system. Several researchers proposed frameworks that would guide cities to take a holistic perspective. This manuscript selects two frameworks and examines through them whether and to what extent broader and more holistic approaches to circular cities are being developed in practice. First, circularity principles, the scope of circular activities, and the concrete circular actions developed in the case study are read through Williams's approach to circular resource management. Second, the spatial circularity drivers framework of Marin and De Meulder is used to elucidate different sustainability framings and spatial practices that dominate contemporary conceptualisations of circularity. These two lenses are applied to five municipalities in Alberta (Canada) that have decided to develop strategies for 'shifting the paradigm' and transitioning to circular cities in 2018. Our study aims to investigate how holistic their roadmaps to circular cities are, and what changes are necessary to move towards more integrated approaches.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.206
Teacher spread0.195 · 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 designTheoretical or conceptual
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

Citations26
Published2022
Admission routes1
Has abstractyes

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