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Record W4297093438 · doi:10.1111/cag.12805

Exploring the associations between cooling centre accessibility and marginalization in Montreal, Toronto, and Vancouver, Canada

2022· article· en· W4297093438 on OpenAlexaffvenueabout
Matthew Quick, Tanya Christidis, Toyib Olaniyan, Nick Newstead, Lauren Pinault

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsCentre for Social InnovationStatistics Canada
Fundersnot available
KeywordsGeographyCity centreRespite careCatchment areaCartographyDrainage basinMedicine

Abstract

fetched live from OpenAlex

Cooling centres provide respite, safety, and social support during extreme heat events for populations that do not have the resources to own or operate in‐home air conditioning. The objective of this study was to measure the spatial accessibility of cooling centres and analyze the associations between cooling centre access and marginalization in Montreal, Toronto, and Vancouver, Canada. The potential spatial accessibility of cooling centres within a 15‐minute walk was measured at the dissemination area scale using the two‐step floating catchment area method. A two‐stage modelling approach was used to analyze the associations between cooling centre access and marginalization. Approximately 62%, 58%, and 54% of the populations in Montreal, Toronto, and Vancouver had access to at least one cooling centre. In Montreal and Vancouver, high marginalization areas were more likely to have cooling centre access than low marginalization areas. Of the areas with cooling centre access, smaller access scores were observed in areas with high residential instability. Approximately one‐fifth of the areas in each city had no cooling centre access and high marginalization, and may be considered for future cooling centres or programs that improve accessibility to existing centres.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.226
Teacher spread0.194 · 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 designObservational
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

Citations5
Published2022
Admission routes3
Has abstractyes

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