Capturing crime, criminals and the public’s imagination: Assembling Crime Stoppers and CCTV surveillance
Bibliographic record
Abstract
This article explores Crime Stoppers’ use of CCTV images as a node of a surveillant assemblage via analysis of a sample of Crime Stoppers advertisements deploying CCTV images supplemented by interviews and other qualitative procedures. Advertisements using images are becoming more prevalent and rely on complex textual narratives and the CCTV image format to construct crime for public consumption to generate ‘tips’. The advertisements capture a narrow range of ‘street crime’ to the benefit of private business and to the neglect of pervasive and serious conduct affecting the less powerful. The convergence of Crime Stoppers and CCTV surveillance is found to have unanticipated and ironic consequences regarding deterrence and identification, to befit a form of ‘counter-law’, and to demonstrate potential to harm individuals and visible minorities. Theoretical implications of this analysis for understanding assumptions about the relation between image and the Truth of crime, governance, and surveillance are discussed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".