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Record W2739466064

Urban Encounters : Art and the Public

2017· book· en· W2739466064 on OpenAlexaboutno aff
Martha Radice, Alexandrine Boudreault‐Fournier, Sebastian Matthias, Susanne Shawyer, Laurent Vernet

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsnot available
Fundersnot available
KeywordsPublic spaceProsperityCreativityImprovisationCreative citySociologyVariety (cybernetics)Visual artsAestheticsUrban spaceContemporary artPublic artArchitectureMedia studiesArtPublic relationsPolitical scienceEngineeringPerformance artArchitectural engineeringRegional science
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.418
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.024
GPT teacher head0.290
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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