How Built Environment Characteristics Influence Social Interactions During Neighbourhood Walks Among Urban Inhabitants
Bibliographic record
Abstract
As an accessible and low-risk mode of transportation and recreational activity, walking both produces and is produced by socio-spatial urban features. The health benefits of walking transcend physical fitness, remaining integral to mental health and to fostering social connectedness in urban communities. Understanding what drives walking behaviour, therefore, warrants attention from a public health perspective. This qualitative case study focuses on the social interactions of inhabitants during neighbourhood walks and how built environment features influence walking patterns and experience. Using diaries, maps, and semi-structured interviews with 45 inhabitants of a mid-sized Canadian city, this research investigates the influence of permanent and temporary physical features on the perceived quality of inhabitants' walks. The findings show the public visibility of urban modifications influences walking behaviour and improves social interactions, leading to a heightened sense of belonging and community. Inhabitant-led modifications in the urban space were mostly neighbourhood-bound and voyeuristic, whereas administrative interventions were more successful for collectivization. Both types of interventions are argued to foster social connectedness through different mechanisms, with positive impacts on inhabitants' health and wellbeing. The findings underscore the relevance of community-led and administratively planned interventions in built environments in positioning public health policies associated with social cohesion and connectedness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".