The association between sidewalk length and walking for different purposes in established neighborhoods
Bibliographic record
Abstract
BACKGROUND: Walking in neighborhood environments is undertaken for different purposes including for transportation and leisure. We examined whether sidewalk availability was associated with participation in, and minutes of neighborhood-based walking for transportation (NWT) and recreation (NWR) after controlling for neighborhood self-selection. METHOD: Baseline survey data from respondents (n = 1813) who participated in the RESIDential Environment (RESIDE) project (Perth, Western Australia) were used. Respondents were recruited based on their plans to move to another neighborhood in the following year. Usual weekly neighborhood-based walking, residential preferences, walking attitudes, and demographics were measured. Characteristics of the respondent's baseline neighborhood were measured including transportation-related walkability and sidewalk length. A Heckman two-stage modeling approach (multivariate Probit regression for walking participation, followed by a sample selection-bias corrected OLS regression for walking minutes) estimated the relative contribution of sidewalk length to NWT and NWR. RESULTS: After adjustment, neighborhood sidewalk length and walkability were positively associated with a 2.97 and 2.16 percentage point increase in the probability of NWT participation, respectively. For each 10 km increase in sidewalk length, NWT increased by 5.38 min/wk and overall neighborhood-based walking increased by 5.26 min/wk. Neighborhood walkability was not associated with NWT or NWR minutes. Moreover, sidewalk length was not associated with NWR minutes. CONCLUSIONS: Sidewalk availability in established neighborhoods may be differentially associated with walking for different purposes. Our findings suggest that large investments in sidewalk construction alone would yield small increases in walking.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".