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Record W2273662266 · doi:10.25710/2p9g-h923

Pedestrian perceptions: a study of the Mount Pleasant neighborhood in Vancouver, B.C., Canada

2020· article· en· W2273662266 on OpenAlexaboutno aff
Jeffery M. Guinn

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsMountPedestrianGeographyPerceptionPsychologyComputer scienceArchaeology

Abstract

fetched live from OpenAlex

Most short distance travel, less than three miles, is being completed by single occupancy vehicles in North America, which leads to many negative effects on the physical environment and citizens' quality of life. Therefore, understanding influences on travel behavior, more specifically non-automotive travel behavior, is crucial. Many researchers and city planners have touted specific factors for encouraging walking and biking, but the body of work to support such notions remains small and fragmented. This study was developed to test all previously identified motivating factors for walking and biking and their relative influence over one's choice. The Mount Pleasant neighborhood in Vancouver, B.C., Canada was chosen as the location for this study because all of the motivating factors were present. Data was collected through a questionnaire-based survey, which also contained demographic and behavioral clarifying questions. In the end, all previous factors were shown to have some influence over one's decision, but some relationships were stronger than others. This work provides a basic outline for future travel behavior studies, and highlights important factors that need further exploration.

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)0.000
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 score0.323
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.024
GPT teacher head0.240
Teacher spread0.216 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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