Using Embodied Videos of Walking Interviews in Walkability Assessment
Bibliographic record
Abstract
The relationship between the built environment and pedestrian travel behavior has inspired many geospatial-based and audit-based indexes for assessing the extent to which an environment is walkable. However, recent research suggests that their accuracy in predicting travel behavior varies depending on the characteristics of the populations being studied—a limitation especially pertinent for those who walk because they lack viable transportation alternatives. Examining the broader scope of walking determinants and mediators from the pedestrian perspective can take into account the mismatch between travel behavior and embodied experience. Social geography provides theoretical avenues for co-analyzing environmental and personal characteristics, while methodological and technological innovations provide ways of placing these theories into practice. A walking interview procedure supported by embodied video recording technology and sedentary interviews was designed to assess the walking environment according to residents’ unique perspectives of their neighborhoods. The walking interview allows for real-time engagement with pedestrians as they experience the environment, while video recording of these engagements offers sensory data that the researcher may use to interpret pedestrian statements and to draw retrospective conclusions. Aided by preceding sedentary interviews, the walking interview also illuminates how a pedestrian’s personal characteristics influence his or her perception of the built and social environment. It was concluded that, as designed, the procedure is well suited to support conventional walkability assessment tools by revealing how pedestrians’ characteristics and recollections shape their engagement with the built environment.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".