{"id":"W4416568361","doi":"10.21428/cb6ab371.e31eeea5","title":"Untangling SNA: The Use and Underuse of Social Network Analysis Among Crime Analysts","year":2025,"lang":"en","type":"article","venue":"CrimRxiv","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Crime analysis; Social network analysis; Law enforcement; Intelligence analysis; Prioritization; Enforcement; Social network (sociolinguistics); Empirical research","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05170059,0.0004787902,0.0004636326,0.006215534,0.004920381,0.009723222,0.002087194,0.001159279,0.001828589],"category_scores_gemma":[0.1702063,0.0006749922,0.0004304865,0.004210933,0.005947071,0.01114317,0.006174192,0.002392976,0.0003468824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005427695,"about_ca_system_score_gemma":0.0109155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06529506,"about_ca_topic_score_gemma":0.1275674,"domain_scores_codex":[0.9544181,0.03004551,0.00166795,0.002283482,0.01009097,0.001493908],"domain_scores_gemma":[0.8190247,0.1349539,0.01843589,0.009839295,0.01407211,0.003674126],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001223395,0.00006611614,0.151495,0.0005208647,0.0001274211,0.0004205169,0.6279519,0.0003943023,0.002533745,0.01140364,0.004803638,0.2001605],"study_design_scores_gemma":[0.00001196151,0.0001491963,0.1011273,0.001556044,0.00009052746,0.0008164483,0.8132983,0.007539921,0.001342072,0.01489042,0.05900692,0.0001707498],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9532168,0.0009786392,0.01533749,0.0139664,0.0001693026,0.00018098,0.0002619945,0.0001751397,0.01571328],"genre_scores_gemma":[0.9870921,0.0006939038,0.0102225,0.0007138159,0.00004740863,0.0001117843,0.0001020194,0.00005520101,0.00096121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9482994,"threshold_uncertainty_score":0.2734221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08300504953085316,"score_gpt":0.3718769388776373,"score_spread":0.2888718893467841,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}