Bibliographic record
Abstract
INTRODUCTION A cultural district (or cultural quarter in Britain and mainland Europe) is a location within a city where cultural and artistic infrastructure, services, activities and networks are concentrated and embedded (Roodhouse 2006). Informally emergent in mixed-use urban neighbourhoods from the embryonic and ad hoc presence of cultural practitioners, cultural districts revalorize built heritage as “hubs, nodes, and workplaces for creative production” in lifecycles that extend from spatialization to recommodification (Zukin & Braslow 2011: 132). In the cultural industries, a cultural hub in a single building is the most “extreme” form of clustering (Pratt 2021). Such cultural hubs, and the industries and practitioners who animate them with their labour, have become important urban economic change agents in cities around the world grappling with industrial decline and restructuring. They adaptively reuse redundant industrial infrastructure (e.g., harbour areas, elevated rail corridors, shipyards, factories, warehouses and storage facilities), offices, schools, hospitals, churches and prisons, turning it into cultural infrastructure that can foster creativity through human co-presence (Pratt 2021). These “creative brownfields” – “aesthetically distinct, derelict and flexible industrial premises” that have attracted the attention of artistic communities and youth subculture – have become formalized as place-making anchors of cultural infrastructure, contributing to structural changes in neighbourhoods and cities (Andres & Golubchicov 2016: 758). This chapter considers the informal and formal dimensions of cultural districts as urban infrastructure. It traces the historical roots of cultural districts to the precarity of artistic careers and the socio-spatial strategy of clustering as a means of professional survival. In the contemporary period, shaped by pandemic lockdowns, venue closures and event cancellations to control the spread of Covid-19, it demonstrates that economic uncertainties have deepened across the culture sector for practitioners and organizations alike, undermining the vitality and viability of cultural districts. Despite the seeming global ubiquity of cultural districts as an urban economic development tool, examples of this cultural infrastructure from Berlin and Leipzig in Germany and Toronto and Hamilton in Canada showcase intermediation in practice, demonstrating the range of cultural district spatial forms, programmes and rationales.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.002 |
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".