The Missing Middle: Filling the Gap Between Walkability and Observed Walking Behavior
Bibliographic record
Abstract
Many fields of study recognize the interdependent health, environmental, and economic benefits of walking. To promote walking in entire populations, measures such as Walk Score have been developed to classify the walking friendliness or walkability of places. Yet high walkability is not always equated with increased walking. This paper investigates this discrepancy with the use of survey data on pedestrian behavior; a variety of geographic information system–derived land use and built environment measures of neighborhoods in Montreal, Quebec, Canada; and socioeconomic characteristics obtained from the 2011 National Household Survey. A descriptive analysis of walking behavior and neighborhood characteristics reveals that some neighborhoods with higher walking rates are characterized by a lower presence of parking lots and setbacks and a greater proportion of on-street tree canopy. Linear regressions predicting walking rates confirm these associations after adjusting for Walk Score and neighborhood socioeconomic characteristics. These findings suggest that more work is needed for nuancing walkability measures and offer particular insight for health professionals, planners, and engineers looking to promote walking as an alternative and healthier mode of transport. Reducing open space, such as parking lots and setbacks, and increasing street-level tree canopy are two ways that the urban built environment can be modified to support walking, especially in areas with high Walk Score and low walking rates.
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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 | 0.022 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".