Mapping policy pathways: Urban referencing networks in public art policies
Bibliographic record
Abstract
This article examines the dynamics of inter-referencing between cities and develops the concept of the ‘Urban Referencing Network’ as a representation of the references made by cities to one another in policy documents. The study employs public art policies, specifically the Percent for Art policy, to investigate the structure of inter-referencing within the urban referencing network. Using a corpus of policy documents from 26 Anglophone cities with over one million residents, we analyse 150 documents containing 2178 inter-references. Combining network measurements and regressions, we explore the emergence of central nodes and the mechanisms influencing their formation. The broader field of arts and cultural policies, with its extensive inter-urban connections and professional networks, provides fertile ground for studying urban referencing networks. By integrating literature on policy mobility and urban networks, this study contributes to a deeper understanding of the circulation of urban ideas and the interplay between cities in policy-making processes. The results demonstrate that only a few cities, including New York, Chicago, London, Seattle, Los Angeles, and Montreal, emerge as central nodes, attracting the other cities’ attention. Attributes of the referenced cities, like economic importance, iconicity and early adoption, determine to a great extent who are the most central nodes.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".