{"id":"W4387087979","doi":"10.1111/1745-9133.12642","title":"“Like aspirin for arthritis”: A qualitative study of conditional cyber‐deterrence associated with police crackdowns on the dark web","year":2023,"lang":"en","type":"article","venue":"Criminology & Public Policy","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Deterrence theory; Enforcement; Law enforcement; Business; Deep Web; Internet privacy; Public relations; Computer security; Engineering; Political science; Law; The Internet; Computer science; World Wide Web","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":[0.01016341,0.0006130327,0.0008676816,0.001592764,0.008787026,0.004026788,0.001620981,0.001975372,0.005063567],"category_scores_gemma":[0.02115486,0.0008336183,0.0003852011,0.001102626,0.01096635,0.00618842,0.005965724,0.004160563,0.0005492176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003806978,"about_ca_system_score_gemma":0.003883255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009150905,"about_ca_topic_score_gemma":0.01775897,"domain_scores_codex":[0.9887866,0.00871696,0.0002711538,0.0005001966,0.000619112,0.001106135],"domain_scores_gemma":[0.9741549,0.01868299,0.002127349,0.0006515612,0.001590619,0.002792491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002351515,0.00005851472,0.002545713,0.00009394754,0.000002646004,0.0005424759,0.9936315,0.00001432538,0.0004388254,0.0006974887,0.0003786194,0.001572372],"study_design_scores_gemma":[0.000002172863,0.00003235932,0.0009656408,0.00007741189,0.000001636203,0.00007908142,0.9959754,0.00005141314,0.0001133275,0.0001332813,0.002560715,0.000007405734],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992768,0.0002235812,0.001090198,0.002429494,0.00005490821,0.0001342236,0.0001095322,0.00001017767,0.003179894],"genre_scores_gemma":[0.9960846,0.0003074644,0.0005374031,0.001030565,0.00002033585,0.000179872,0.00004417678,0.00002516661,0.00177047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01016341,"threshold_uncertainty_score":0.05374992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.152878991825361,"score_gpt":0.3654467470855202,"score_spread":0.2125677552601592,"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."}}