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Equitable Implementation of Green Infrastructure

2023· article· en· W4387058136 on OpenAlex
Nicole Jang, Andréanne Doyon

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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Planning and Policy / Aménagement et politique au Canada · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsOperationalizationEquity (law)Green infrastructureClimate changeEnvironmental planningBusinessPolitical sciencePublic relationsGeographySociologyEnvironmental resource managementEconomicsEcology

Abstract

fetched live from OpenAlex

As climate change continues to pose a threat to human health, cities have turned to nature-based solutions, such as green infrastructure (GI), to lessen the impacts of climate change felt by communities. However, many practitioners are not incorporating equity considerations in GI siting decisions; thus, leaving marginalized and racialized communities to disproportionately bear the impacts of urban environmental issues. In the City of Vancouver, British Columbia, Canada, the GI Branch is investigating ways in which they can apply an equity lens to their work. To aid in their endeavour, this study examines existing challenges to equitably implementing GI, as well as areas for improvement, through a literature review, document and planning tool analysis, and key informant interviews. Drawing from the findings, this paper develops a set of equity criteria, which centre three dimensions of social equity: distributional, recognitional, and procedural equity, to help practitioners operationalize equity in GI project evaluations.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.304
Teacher spread0.292 · 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