Strengthening Social Ties While Walking the Neighbourhood?
Bibliographic record
Abstract
Social connectedness among neighbours impacts health and well-being, especially during stressful life events like a pandemic. An activity such as neighbourhood walking enables urban inhabitants to engage in incidental sociability and acts of “neighbouring”—that is, authentic social interactions with neighbours—to potentially bolster the social fabric of neighbourhoods and strengthen relationships. With the potential of neighbourhood walking in mind, this article investigates how everyday encounters while engaged in routine neighbourhood walks strengthen and/or weaken social ties among neighbours. To this end, the article draws on three sources of qualitative data from neighbourhood walkers in Southwestern Ontario, Canada: (a) “walking diaries” in which participants took note of their walking routes, the people they observed on their walks, and other details of their walking experiences; (b) maps of their neighbourhoods that outlined the boundaries of their self-identified neighbourhoods, their routine walking routes, and the people they recognized during their neighbourhood walks; and (c) one-on-one interviews during which participants provided crucial context and meaning to the maps and their walking experiences. The findings provide evidence of how interactions among inhabitants, while engaged in neighbourhood walking, help generate greater social 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".