A Shared Everyday Ethic of Public Sociability: Outdoor Public Ice Rinks as Spaces for Encounter
Bibliographic record
Abstract
Everyday life in urban public space means living amongst people unknown to one another. As part of the broader convivial turn within the study of everyday urban life (Wise & Noble, 2016), this article examines outdoor public ice rinks as spaces for encounter between strangers. With data drawn from 100 hours of naturalistic and participant observation at free and accessible outdoor public non-hockey ice rinks in two Canadian cities, we show how ‘rink life’ is animated by a shared everyday ethic of public sociability, with strangers regularly engaging in fleeting moments of sociable interaction. At first glance, researching the outdoor public ice rink may seem frivolous, but in treating it seriously as a public space we find it to be threaded through with an ethos of interactional equality, reciprocal respect, and mutual support. We argue that the shared everyday ethic of public sociability that characterizes the rinks that we observed is a function of the (1) public and (2) personal materiality required for skating; (3) the emergence of on ice norms; (4) generalized trust amongst users; (5) ambiguities of socio-spatial differentiation by skill; and (6) flattened social hierarchies, or what we call the quotidian carnivalesque. Our data and analysis suggest that by drawing together different generations and levels of ability, this distinct public space facilitates social interactions between strangers, and so provides insights relevant to planners, policy makers and practitioners.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".