Capturing crime, criminals and the public’s imagination: Assembling Crime Stoppers and CCTV surveillance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it