Walking beyond the city? On the importance of recreational mobilities for landscape planning, urban design, and public policy
Bibliographic record
Abstract
Walking engenders many descriptive, normative, and speculative debates. This article reviews work done in the interventionist realms of landscape planning, urban design, and public policy, where attention is increasingly being paid to walking (as a matter of fact) and its often-prescriptive corollary of ‘walkability’ (as a matter of concern). What patterns of critical engagement are seen in work on how, why, and where people walk? I explore how (a) the so-called compact city is seen as the only context where walking and other ‘soft’ modes of everyday mobility meaningfully occurs, and (b) scholarly debates on self-propelled movement seem to focus too narrowly on necessary or utilitarian activity. Recreational mobilities at various temporal and spatial scales thus tend to be overlooked or ignored altogether. Drawing on the interdisciplinary explorations presented in this special issue of Mobilities, a provisional agenda for research and practice is presented. Suggestions are made as to how one might approach the dense, compact city (as phenomenon and as normative impulse in spatial planning) in new ways by foregrounding walking as a widespread example of ‘discretionary’ mobility, i.e., as optional movement in space.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".