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Record W2086793259 · doi:10.2495/uw140251

The St George Rainway: building community resilience with green infrastructure

2014· article· en· W2086793259 on OpenAlexaffabout
Joseph Welsh, Peter Mooney

Bibliographic record

VenueWIT transactions on the built environment · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRealmCohesion (chemistry)ArchitectureLandscape architecturePsychological resilienceCivil engineeringArchitectural engineeringSociologyEnvironmental resource managementEnvironmental planningPolitical scienceEngineeringGeographyEnvironmental sciencePsychology

Abstract

fetched live from OpenAlex

Central to global climate change and central to the profession of landscape architecture is the element: water.The St George Rainway offers a new opportunity to be a demonstration project for the City of Vancouver, Canada, where the city and its community of Mount Pleasant act as collaborators with design, construction, and maintenance of a project with water in the public realm.The voluntary engagement in the physical transformation of one's community can provide opportunity for a growth in social cohesion.Subsequently, this growth can improve the conditions that fostered the bonds and bridges within that community that inspired the initial engagement.Green infrastructure, when considered through this lens, has a reciprocal relationship with social cohesion, where the improvement of one feeds the improvement of the other.This model could therefore provide both a resilient option for physical development of land and for social development of community for neighbourhoods by encouraging more interaction among neighbours and with their local public realm.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0050.005
Open science0.0010.009
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.230
Teacher spread0.219 · 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 designNot applicable
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

Citations4
Published2014
Admission routes2
Has abstractyes

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