Urban interventionism as a challenge to aesthetic order: Towards an aesthetic criminology
Bibliographic record
Abstract
This article is concerned with ideas of urban order and considers the scope for playing with people’s expectations of order. In particular, drawing on criminological, philosophical and urban studies literatures, the article explores the notion of aesthetic order. The power to dictate aesthetic order is highlighted. The example of urban interventionism is used to consider those that challenge an approved aesthetic order. Here the article draws on cultural criminology and visual criminology, with illustrations coming from research in Toronto, Canada. Influenced by Alison Young’s (2014a) conceptualisation of ‘cities within the city’, the article considers how different people using the same space have different or overlapping ways of understanding aesthetic order. Of relevance to criminology, it is contended that people or things that contravene an approved aesthetic order may face banishment and criminalisation. It is concluded that respect for such difference is required. An aesthetic criminology is suggested.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.166 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".