Black in blue networks: social network integration and racial disparities in police use of force
Bibliographic record
Abstract
Abstract Explanations for police behavior argue that “us versus them” group dynamics shape officer interactions with the public. Yet, studies on racial disparities in policing overlook the interpersonal networks central to scholarship on group boundaries. We integrate insights from the literature on networks, group identity, and intergroup relations to consider how social network size and racial composition affect racial disparities in police officer use of force, and how those social network effects are conditioned by officer race. We test our perspective by analyzing newly collected longitudinal network data on the friendship relations between officers in one large department and linking these data to administrative records on officer use of force. The number of friendship ties to other officers is associated with within-officer increases in use of excessive force against Black victims, but not against White victims. Ties to White officers are only associated with use of excessive force against Black victims and only among Black officers. These findings suggest that social network integration contributes to racial disparities in police use of force and carries broader implications for intra- and intergroup discrimination in organizations characterized by strong institutional attachments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".