Partners in Force? Understanding Police Use of Force from a Network Perspective
Bibliographic record
Abstract
Objectives The importance of peer relations is rooted in decades of policing research; however, scholars have largely overlooked the role of peers in officers’ use-of-force behaviors. The current study investigates the “connected” nature of police use of force. Methods Data on officers’ networks are reconstructed from 11,834 use-of-force reports involving 1,894 officers in seven departments in New Jersey. Exponential Random Graph Models evaluate which officer-level attributes and network dependencies are associated with officers’ co-involvement in police use-of-force incidents. Results Findings indicate the police use of force is not evenly distributed but concentrated on a subset of officers and partnerships. Variation in officers’ likelihood of using force together is driven by individual characteristics, including officer race/ethnicity, rank, and tenure. In addition, co-involvement in force clusters among officers, with officers likely to engage in force together when they share a connection. Conclusion This study highlights an alternative starting point for understanding police use of force. By paying greater attention to the structural makeup of the department, such as the connectivity of the force network, agencies can design efforts that aim to reduce incidents of force through relational properties such as assignments and partnerships.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".