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Record W4401592617 · doi:10.1002/9781394312498.ch4

Walking in Everyday Life

2024· other· en· W4401592617 on OpenAlexaff
Marie‐Soleil Cloutier, Karine Lachapelle

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPedestrianWalkabilityPsychological interventionField (mathematics)Transport engineeringArchitectural engineeringComputer sciencePsychologyEngineeringBuilt environmentCivil engineering

Abstract

fetched live from OpenAlex

Walking is the very essence of biped human beings, one of the first accomplishments we expect of our infants and the last thing we let go of as we age. This chapter briefly reviews the five dimensions characterizing walkability in the urban environment, based on recent works on the subject. It presents the different sources of pedestrian insecurity, in particular the risk pedestrians incur in relation to road traffic and its most well-known influencing factors. The chapter focuses on two interventions (Vision Zero and speed reduction) aimed at enhancing pedestrian safety. It discusses two avenues of research that require greater attention from the scientific community and the actors in the field: accurate measurements of pedestrian exposure and the evaluation of interventions. The chapter also highlights the main elements that need to be improved in our living environments, so that everyone can walk safely along the existing road network.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.005

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 head0.023
GPT teacher head0.319
Teacher spread0.296 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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