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 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 | 0.052 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".