The Impact of Correctional CCTV Cameras on Infractions and Investigations: A Synthetic Control Approach to Evaluating Surveillance System Upgrades in a Minnesota Prison
Bibliographic record
Abstract
Internal surveillance systems have long been used by prisons to combat misbehavior. Yet, limited research has focused on cameras’ preventative potential, failing to examine their utility in investigations. Using comparative interrupted time-series analyses and synthetic control methods, this study evaluates the impact of upgrading a surveillance system in a prison’s housing unit on total infractions and infractions resulting in guilty dispositions. Upgrades were two-phased, allowing us to examine the differential effects of replacing outdated cameras versus installing new cameras. One comparison unit came from the same facility as the treatment unit, while the other was synthetically generated from units in other prisons. We found limited evidence that the interventions reduced infractions, though there was a stronger link between the interventions and an increase in guilty dispositions, particularly from the installation of new cameras to reduce blind spots. We discuss the implications of these findings for policy and research.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".