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Record W4396760868 · doi:10.1080/1573062x.2024.2351856

Increasing knowledge and trust to overcome barriers to green infrastructure implementation: a Vancouver case study

2024· article· en· W4396760868 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueUrban Water Journal · 2024
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGreen infrastructureBusinessEnvironmental planningTransport engineeringEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

This paper presents a case study of the obstacles to green infrastructure implementation in the City of Vancouver. This case study has two aims, 1) to better understand the planning and decision-making processes hindering the widespread implementation of green infrastructure across the City of Vancouver, and 2) propose solutions to facilitate the uptake of GI. This paper begins by reviewing existing literature on barriers to green infrastructure implementation. Then, it investigates current perceptions of GI in the City of Vancouver, and what barriers exist to its implementation through document analysis and semi-structured interviews. Proposed solutions to green infrastructure implementation are reviewed and recommendations are provided for the City of Vancouver. It ends with a short discussion on the applicability of lessons learned from the study of Vancouver for other municipalities seeking to overcome barriers to green infrastructure implementation.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.390
Teacher spread0.371 · 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