On to the next one? Using social network data to inform police target prioritization
Bibliographic record
Abstract
Purpose Target prioritization is routinely done among law enforcement agencies, but the criteria to establish which targets will lead to the most crime reduction are neither systematic, nor do they take into account the networks in which offenders are embedded. The purpose of this paper is to propose network capital as a guide for prioritization exercises. The approach simultaneously considers a participant’s network centrality and their crime-affiliated attributes. Design/methodology/approach Data on all police interactions are used to map the social networks of two mutually connected police targets from a mid-size city in British Columbia, Canada. Network capital is captured by combining the extent to which individuals act as brokers between otherwise unconnected individuals (betweenness centrality), their number of contacts in the network (degree centrality), and whether they have a criminal record, gang ties, and a firearm carrier status. Findings The network comprises 101 associates, with nine mutual contacts amongst the two targets, and half of the network having a crime-affiliated attribute. Network capital directed the prioritization process to seven associates who stood out. Targeting strategies from two different investigative outcomes are compared. Research limitations/implications The specific recommendations of the study can only be interpreted within the context of the initial targets around which the network was constructed. As a prioritization approach, however, network capital is generalizable to other contexts with implications for law enforcement officials and, more broadly, the community. Originality/value The study provides insights into the practical application of network analysis with already existing police data. Network capital is data driven, which comes with its own limitations, but which constitutes an improvement over purely informal approaches to target prioritization.
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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 | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".