Creative improvement, cultural infrastructure and urban zones: a tale of three cities and their cultural districts
Bibliographic record
Abstract
This paper critically examines place governance and cultural policy decision-making in the context of three cities - Melbourne, Manchester and Toronto. It takes an infrastructural lens to examine the narrative histories and policy rationales for creating spatial zones in which culture is demarcated as an engine for social, cultural and economic development in each of these cities. By contextualising each city in relation to their individual histories, social, economic and political dimensions, the paper offers insights into the global similarities and differences in cultural district development, exposing a reliance on interests and actors that extend far beyond the state. It finds that the zoning of cultural space intermediates these interests requiring balance and oversight to ensure democratic participation and urban justice. • The paper offers a comparative analysis of three cases of cultural ‘zoning’ in city-regions that have dominant ‘creative city’ narratives. • It is based on empirical research including semi-structured interviews, ethnography and policy analysis. • Literature review produces interdisciplinary analytical frame using concepts such as ‘extrastatecraft’, ‘zoning’ and place governance’. • It moves beyond arguments concerning the success or otherwise of cultural districts to explore the spatial dynamics of governance and statecraft.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".