{"id":"W2167719512","doi":"10.1108/pijpsm-06-2016-0079","title":"On to the next one? Using social network data to inform police target prioritization","year":2017,"lang":"en","type":"article","venue":"Policing An International Journal","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Betweenness centrality; Centrality; Law enforcement; Context (archaeology); Social network analysis; Social network (sociolinguistics); Social capital; Computer security; Computer science; Prioritization; Business; Process (computing); Data science; Knowledge management; Political science; Process management; Law; Geography; Social media; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00773381,0.0004711054,0.0003342971,0.009956385,0.001476988,0.003789347,0.001065279,0.0007758607,0.00319206],"category_scores_gemma":[0.04812735,0.0003539059,0.0002415327,0.006430088,0.0009408241,0.007175856,0.002989121,0.001061981,0.0007181509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002606571,"about_ca_system_score_gemma":0.002892213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03441592,"about_ca_topic_score_gemma":0.06231032,"domain_scores_codex":[0.9944907,0.003425675,0.000318007,0.0007957248,0.0007067095,0.0002631603],"domain_scores_gemma":[0.9668792,0.01958669,0.006774467,0.002259934,0.003362453,0.001137187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001789611,0.0002166264,0.6819856,0.0008193763,0.000202232,0.0003949878,0.02561516,0.007713071,0.001370765,0.02487512,0.01644173,0.2401864],"study_design_scores_gemma":[0.000092617,0.0003741111,0.406123,0.002409345,0.0002911819,0.0005741791,0.1675968,0.1795734,0.004997392,0.1183357,0.1193583,0.0002740111],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8681273,0.001064718,0.0794518,0.01041312,0.0001278685,0.001181254,0.01086572,0.0003716099,0.02839666],"genre_scores_gemma":[0.9399154,0.0005869587,0.05373774,0.0004155494,0.00004748672,0.000553752,0.00348229,0.00004166722,0.001219195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03441592,"threshold_uncertainty_score":0.0684312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3390713836111242,"score_gpt":0.5037575485793544,"score_spread":0.1646861649682302,"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."}}