The relationship between walk score® and perceived walkability in ultrahigh density areas
Bibliographic record
Abstract
Walk Score® is a free web-based tool that provides a walkability score for any given location. A limited number of North American studies have found associations between Walk Score® and perceived built environment attributes, yet it remains unknown whether similar associations exist in Asian countries. The study's objective is to examine the covariate-adjusted correlations between the Walk Score® metric and measures of the perceived built environment in ultrahigh density areas of Japan. Cross-sectional data were obtained from a randomly selected sample of adult residents living in two Japanese urban localities. There was a large correlation between Walk Score® and access to shops (0.58; p < 0.001). There were medium correlations between Walk Score® and population density (0.38; p < 0.001), access to public transport (0.34; p < 0.001), presence of sidewalks (0.41; p < 0.001), and access to recreational facilities (0.37; p < 0.001), and there was a small correlation between Walk Score® and presence of bike lanes (0.16; p < 0.001). There was a small negative correlation between Walk Score® and traffic safety (-0.13; p < 0.001). There was a medium correlation between Walk Score® and overall perceived walkability (0.48; p < 0.001). This study's findings highlight that Walk Score® was correlated with several perceived walkable environment attributes in the context of ultrahigh density areas in Asia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".