Urban Referencing Styles and Networks: How Cultural Domination and Local Interests Shape Policy Discourse
Bibliographic record
Abstract
In the global arena of municipal policymaking, cities do not merely address local concerns but actively engage with other cities in a global relational space, referencing and being referenced by others. Within these networks, certain “model cities” emerge, linking urban transformation strategies to specific city experiences. Although much research focuses on the production of model cities—how they gain prominence and status—less attention has been given to the peer cities that reference them. The authors examine how model cities rise by analyzing “referential styles”: the ways cities express interest in one another. Drawing on urban sociology, cultural theory, and network analysis, the authors propose two propositions to explain the forces that influence referencing styles: the cultural domination proposition, which suggests that the characteristics of referenced cities shape how they are discussed, and the networks from culture proposition, which suggests that referencing cities’ attributes drive their interpretations. Using public art policy documents (1959–2020) from 26 major anglophone cities and computational techniques, the authors investigate the referential styles cities use to discuss one another. The authors find support for both propositions: although dominant cities determine where to look, it is often the attributes of referencing cities that determine how to look that shape the referencing style. These results suggest orienting policy-mobility research more toward the peer-network ecologies that actively construct urban meaning.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".