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Record W2539806169 · doi:10.4000/articulo.2974

Emerging Narratives of a Sustainable Urban Neighbourhood: The Case of Vancouver’s Olympic Village

2016· article· en· W2539806169 on OpenAlexaffabout
Lisa Westerhoff

Bibliographic record

VenueArticulo – revue de sciences humaines · 2016
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsUniversity of GuelphUniversity of British Columbia
Fundersnot available
KeywordsNeighbourhood (mathematics)NarrativeSustainabilityExperiential learningSociologySustainable livingGeographyEconomic growthEnvironmental planningPolitical scienceEconomicsEcology

Abstract

fetched live from OpenAlex

Vancouver’s Olympic Village neighbourhood has been credited with playing an important role in shifting the city towards a more comprehensive approach to sustainability. Like many other urban sustainability efforts at the neighbourhood scale, however, little is known as to the actual performance of the neighbourhood from the perspective of its occupants. To help fill this gap, I present a framework for the evaluation of the performance of sustainable neighbourhoods that that combines insights from narrative and social practice theories to explore how certain narratives of sustainable living are created, translated into practice, and play out in the lives of the principal constituents they affect. In doing so, I begin to reveal the qualitatively felt, experiential dimensions of being in this new form of development, with important lessons for the design, construction and management of future sustainable neighbourhood projects. The study shows that a narrative of liveability and the consideration of short-term quality of life benefits is central to the achievement of ecological and emissions goals. However, an in-depth consideration of the needs of lower income populations is necessary to ensure that the benefits of sustainable living are distributed evenly across socio-economic tiers.

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)0.000
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 score0.586
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.010
GPT teacher head0.243
Teacher spread0.234 · 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

Citations10
Published2016
Admission routes2
Has abstractyes

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