Urban Encounters : Art and the Public
Bibliographic record
Abstract
Public art is on the urban agenda. Given recent claims about the importance of creativity to urban prosperity, opportunities for installing or performing art in the city have multiplied. As cities strive to appear culturally dynamic, the stakes of artistic production rise higher than ever. Exploring the interaction between art and the public in Canadian cities, Urban Encounters features writing by artists, architects, curators, anthropologists, geographers, and urban studies specialists. They show how people and places affect the structure and content of public artworks, what kinds of urban spaces and socialities are generated through art, and how to investigate and interpret encounters between art and its viewers in the city. Discussing a variety of art forms, including mobile cinemas, street improvisation, audiovisual investigations, and assembled objects, the contributors treat public artworks not just as aesthetic installations, but as agents that participate in the social and cultural evolution of cities. Using original, hands-on approaches, Urban Encounters reveals how art in the urban public space generates encounters that can transform both the city itself and the ways that people relate to it. -- Publisher's website.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".