Spaces of Sociability: Enhancing Co-presence and Communal Life in Canada
Bibliographic record
Abstract
Digital technologies have transformed how we connect and socialize. Although virtual spaces command much of our attention, physical spaces remain essential to our everyday lives. This report synthesizes existing research on public spaces that potentiate, facilitate, and enhance relations between people beyond networks of primary relations, to better understand where sociability between strangers happens, where it does not, and how it may be enhanced. As central spaces of sociability, public spaces are an essential part of our social infrastructure.bAs spaces of sociability, public spaces improve quality of life by increasing opportunities for social contact, learning, leisure, play, and simply sharing space with strangers. Sociable public spaces facilitate interactions across social difference and create belonging; they can be both planned and flexible, and support a range of uses that respond to local needs and residents. The best sociable public spaces attend to historical, social, cultural, and community context; they include careful planning and programming and facilitate playfulness and improvised uses; they attend to basic human needs and foreground accessibility in multiple ways. To make public spaces better spaces of sociability, planners and policy makers need better more granular data on the social life of public spaces. Investments in public spaces as social infrastructure that supports diverse populations will counter social isolation, social fragmentation, and political polarization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".