Designing Space for Walking as the Primary Mode of Travel
Bibliographic record
Abstract
Walking and urban spaces are a matter of course, in the era of so-called soft, sustainable, or active mobility, planners, city planners, architects, landscape architects, and other actors who create the city, attributing many virtues to this way of moving or getting around. This chapter discusses the diversity of research approaches to walking. It focuses on the physical–spatial determinants of walkability in relation to the perceptions and emotions of walkers. The chapter highlights some of the challenges of scales of analysis in a perspective of intervention on living environments. The social experience of walking is juxtaposed with the sensory experience. The advantage of the urban atmosphere analysis is that it allows a comparison between purely objective approaches that analyze the morphofunctional determinants of the walking environment and subjective analyses that focus on either the sensory reactions of the body or the sociological dimensions associated with the walking experience.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →1 of 3 models called this metaresearch. This work is contested: it sits on the field's empirical boundary, and whether it counts depends on which model you asked. It is one of the 51 works in the disagreement dossier.
Chapter whose stated purpose is to discuss the diversity of research approaches to walking, comparing objective and subjective analytic traditions; boundary between a methods review and a domain review.
This chapter examines walking and urban design, not research itself.
Urban design chapter on walking as travel mode, not science-of-science.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".