MétaCan
Menu
Back to cohort
Record W4402378021 · doi:10.1080/03081060.2024.2399635

Access to green and gray urban nature amenities: exploring equity in Montreal's built environment

2024· article· en· W4402378021 on OpenAlexafffundabout
José Arturo Jasso Chávez, Geneviève Boisjoly, Kevin Manaugh

Bibliographic record

VenueTransportation Planning and Technology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsPolytechnique MontréalMcGill University
FundersMitacs
KeywordsTransport engineeringEquity (law)Built environmentRegional scienceGeographyEngineeringBusinessEnvironmental planningPolitical scienceCivil engineering

Abstract

fetched live from OpenAlex

Contemporary ideologies have identified two main paradigms contributing to sustainable cities: green urban and gray urban nature. The first refers to features that make cities greener, such as parks or tree coverage. The second refers to gray but sustainable areas, such as dense areas or the proximity to key amenities that can foster active travel. Accessibility to green and gray urban nature amenities by active transport is essential for evaluating if cities are sustainable. We analyzed the accessibility to green (green spaces) and gray (food stores and pharmacies) urban nature in Montreal. The findings show that most people have sufficient access to green and gray urban nature, and low-income groups have better access than high-income groups. Areas with access to both tend to be highly dense, suggesting that dense areas have sustainable attributes. Transport planning interventions in areas lacking access are necessary to achieve sustainable goals in Montreal.

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)0.003
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 score0.197
Threshold uncertainty score0.397

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.041
GPT teacher head0.309
Teacher spread0.268 · 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

Citations2
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueTransportation Planning and TechnologySame topicUrban Green Space and HealthFrench-language works237,207