Managing Risk and Preempting Immorality in Private Employment of Public Police
Bibliographic record
Abstract
This article examines risk management and moral regulation of private employment of public police (or PEPP). Drawing on a study of 104 North American police departments and analysis of interviews with police and private employers, police policies and procedures, and police assignment logs, we first identify PEPP contexts. We then argue that risk management is as much of as by public police officers. This risk management is sometimes preempted by moral regulation of police officers focused on objects, spaces, and suspect employers and which partially aims to preserve police legitimacy. We then discern four means of managing risk: department-coordinated assignments, officer reporting for superior assessment, private user liability insurance for temporarily hired officers, and opportunistic third-party commercial brokers. The article makes an empirical contribution by exploring risk management and moral regulation of PEPP and a conceptual contribution by lending more understanding to risk management and moral regulation in sociolegal studies.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".