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Record W4386137831 · doi:10.1080/02723638.2023.2243132

Assembling place-based transitions: capitalist logics of green building in Vancouver, Canada

2023· article· en· W4386137831 on OpenAlexaboutno aff
Kirstie O’Neill, Julia Affolderbach

Bibliographic record

VenueUrban Geography · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Planning and Landscape Design
Canadian institutionsnot available
FundersFonds National de la Recherche LuxembourgDeutsche Forschungsgemeinschaft
KeywordsEconomic geographySociologyPolitical scienceArchitectural engineeringPolitical economyGeographyEngineering

Abstract

fetched live from OpenAlex

Green building is increasingly central in urban sustainability strategies to reduce greenhouse gas emissions, and to demonstrate leadership, innovation, and technological advances. Vancouver offers a strategic example of a city that has adopted green building policies for sustainability and boosterism purposes. We combine assemblage thinking with sustainability transitions research to expose the relationality and interconnectedness of green building practices in specific places like Vancouver. This allows us to explore the entangled nature of niche-regime relations, and the stickiness between places and practices, which influence emergent innovations: how place specificity affects the unfolding of transitions. Through our empirical examples, we identify two logics driving green building in Vancouver (risk and innovation, and urban entrepreneurialism), which although differentiated nevertheless work to reinforce neoliberal sustainability activities. We argue that despite implementation of varied green building approaches, policy and discourse tend to mainstream a weaker, incremental form of green building.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.138
Threshold uncertainty score1.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0280.020
Scholarly communication0.0100.002
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.193
Teacher spread0.184 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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