Cracks in the Creative City: The Contradictions of Community Arts Practice
Bibliographic record
Abstract
Abstract The recent flurry of research about arts‐led regeneration initiatives illuminates how contemporary arts festivals can become complicit in the production of urban inequality. But researchers rarely engage with detailed empirical examples that shed light on the contradictory role that artists sometimes play within these spectacularized events. Similar research in performance studies connects the political limits and potential of social practice arts — interventions that encourage artists and non‐artists to co‐produce work — as civic boosters strive to stage cities in order to attract investment. In this article, I explore the case study of Streetscape: Living Space at Regent Park , a participatory artistic intervention programmed in a public housing neighbourhood that is undergoing redevelopment in Toronto, C anada. Streetscape was part of the Luminato festival, an elite booster coalition‐led festival of ‘creativity’. I refer to these arts interventions to demonstrate how artists engaging in social practice arts can become complicit in naturalizing colonial gentrification processes at multiple scales. But I also reveal how artists can leverage heterogeneous arts‐led regeneration strategies to make space for ‘radical social praxis’ ( K won, 2004), interventions that challenge hegemonic regimes. I conclude by interrogating the effectiveness of place‐based efforts in unsettling the ‘creative city’.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.023 | 0.122 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".