Risky Spaces and Dangerous Faces: Urban Surveillance, Social Disorder andCCTV
Bibliographic record
Abstract
Since the early 1990s, there has been a steady increase in the use of closed circuit television cameras to monitor public space(s) across Europe and North America. The existing theoretical literature has tended to explain the resort to CCTV in the context of disciplinary subjection. Whereas one set of studies explains CCTV surveillance using the metaphor of panopticon, more recent argumentation has identified CCTV as a ‘social ordering strategy’ which serves the interests of elite/business partnerships through risk-based modes of neoliberal regulation. This article provides insight into the hitherto neglected emotional and affective dimensions of the adoption of CCTV monitoring programs, privileging the role of social antagonism in the consolidation of public surveillance schemes. Developing one explanation for the ascension of open-street monitoring which advances the literature beyond the dominant materialideological perspective(s), the article engages insights from Foucauldian and psychoanalytic theory to explicate the reciprocal functioning of grievance and risk-based modes of problematization, set in a wider imaginary web of relations, in the symbolic constitution of social disorder as a mechanism of affective governance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".