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Record W4226207651 · doi:10.1080/07352166.2022.2038033

The space of ideas: Public art policy and the concept of urban model spaces

2022· article· en· W4226207651 on OpenAlex
Noga Keidar, Daniel Silver

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Urban Affairs · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsUniversity of TorontoThe Scarborough Hospital
Fundersnot available
KeywordsOptimal distinctiveness theoryTemporalityPublic policyUrban policySociologySpace (punctuation)Regional scienceUrban planningEconomicsEpistemologyComputer scienceEconomic growthEcology

Abstract

fetched live from OpenAlex

The urban policy mobility literature describes the widespread circulation of policy ideas while highlighting their mutations along the way. At the same time, the literature often analyzes the localization of such ideas by examining their adoption in one or several cities. To better understand policy replications and mutations, we develop theoretical and methodological strategies that provide sensitivity to both local distinctiveness and global variability. We build on the Urban Policy Mobility literature and combine it with ecological theories of conceptual spaces to develop the concept of Urban Model Spaces—a matrix of discursive possibilities evolving from the accumulated replications and localizations of a model. We articulate it via three core properties central to Urban Policy Mobility—Temporality, Scale, and Position—and test how they shape the emergence of policy discourses. To demonstrate the concept we analyze public art policy and the funding mechanism of the Percent for Art ordinance from 26 cities combining Structural Topic Modeling and regression analysis.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.741
Threshold uncertainty score0.603

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.270
Teacher spread0.242 · 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