{"id":"W2053858564","doi":"10.1057/sj.2009.18","title":"Comparing the ties that bind criminal networks: Is blood thicker than water?","year":2009,"lang":"en","type":"article","venue":"Security Journal","topic":"Crime, Illicit Activities, and Governance","field":"Social Sciences","cited_by":80,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Canadian Mounted Police","funders":"","keywords":"Kinship; Organised crime; Criminology; Exponential random graph models; Business; Political science; Sociology; Law; Graph; Computer science; Random graph","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.005849647,0.0003663228,0.0006859779,0.003119141,0.003425949,0.005530547,0.001220484,0.002147835,0.01807908],"category_scores_gemma":[0.0685154,0.0003985424,0.0003883868,0.004068292,0.005830639,0.01019793,0.004873835,0.001967101,0.0009214621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001719497,"about_ca_system_score_gemma":0.001949298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01867996,"about_ca_topic_score_gemma":0.02694782,"domain_scores_codex":[0.9937599,0.003746406,0.0002036505,0.0004772292,0.0006187593,0.001194004],"domain_scores_gemma":[0.9688395,0.01715505,0.006786099,0.001895672,0.00226435,0.003059257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001583361,0.000420283,0.6713021,0.0004171324,0.0008503198,0.0003505987,0.03828212,0.001709499,0.001215257,0.1664738,0.009362443,0.1080332],"study_design_scores_gemma":[0.0001797177,0.0004375619,0.6012126,0.0007620432,0.001038211,0.0002381778,0.1202458,0.00305011,0.0007209828,0.2379927,0.03402884,0.00009318432],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9345822,0.001747478,0.003896521,0.009163675,0.0001604142,0.00006838938,0.0003741074,0.0000169086,0.04999026],"genre_scores_gemma":[0.9974949,0.0003750434,0.0003590401,0.0005297814,0.00004453298,0.00002546511,0.00008388005,0.00001120029,0.001076025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01867996,"threshold_uncertainty_score":0.06048054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04168272282065615,"score_gpt":0.288443451013333,"score_spread":0.2467607281926769,"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."}}