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Record W4408317885 · doi:10.1111/cag.70005

Top pedestrian concerns in Canada mapped on WalkRollMap.org

2025· article· en· W4408317885 on OpenAlexafffundvenueabout
Colin Ferster, Karen Laberee, Trisalyn Nelson, Meghan Winters, Marie‐Soleil Cloutier, Daniel Fuller

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Mobility and Location-Based Analysis
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversity of SaskatchewanSimon Fraser UniversityUniversity of Victoria
FundersCanadian Institutes of Health ResearchPublic Health AgencyPublic Health Agency of Canada
KeywordsPedestrianGeographyArchaeology

Abstract

fetched live from OpenAlex

Abstract Walking is a healthy, sustainable, and economical form of transportation or recreation. Yet in North America walking is not always accessible, safe, or comfortable. A challenge to creating quality pedestrian environments is lack of data on what barriers exist and how barriers vary across communities. Our goal is to characterize pedestrian barriers and concerns at the microscale level. We analyzed 2,588 reports of hazards, missing amenities, or incidents from WalkRollMap.org , a crowdsourced webmap of barriers to walking or rolling. We assigned themes related to actionable infrastructure interventions and summarized data by location, walkability, street type, and characteristics of who reported it (age, gender, and self‐reported disability). Most reports were related to crossings (45%), sidewalk quality (29%), and the volume and speed of cars (13%). Reports were more common in more walkable places (likely related to exposure) and on major roads. People living with a disability reported sidewalk concerns at a higher rate than others, while people over 75 years of age were more likely to identify issues related to the volume and speed of cars. Cities should prioritize risk reduction interventions for pedestrian road crossings and sidewalk improvements, especially on major roads in amenity dense walkable places.

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 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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.010
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.242
Teacher spread0.229 · 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 teacher head, not a consensus.

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

Citations0
Published2025
Admission routes4
Has abstractyes

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