Untangling SNA: The Use and Underuse of Social Network Analysis Among Crime Analysts
Bibliographic record
Abstract
While research has long demonstrated the potential of social network analysis (SNA) for criminal intelligence, empirical studies have revealed a growing gap between theory and practice. This study examines the role, prospects, and challenges of using SNA in criminal intelligence, addressing two primary questions: (1) How is SNA being used in criminal intelligence units in law enforcement agencies? and (2) What do analysts perceive as the challenges in SNA’s integration in policing? Semi-structured interviews were conducted with 16 Canadian crime analysts who reported experience with SNA. The findings highlight that analysts utilize SNA mainly for visualization and target prioritization purposes. However, analysts frequently reported a gap between the perceived potential of SNA and their ability to incorporate it into routine intelligence. In response to these challenges, analysts suggested required areas for reform, such as comprehensive and tiered training, and automated software to support the integration of SNA.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".