{"id":"W2159188135","doi":"10.1002/meet.1450440231","title":"Tools of the trade: Drugs, law and mobile phones","year":2007,"lang":"en","type":"article","venue":"Proceedings of the American Society for Information Science and Technology","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mobile phone; Internet privacy; Law enforcement; Mobile technology; Context (archaeology); Perspective (graphical); Enforcement; Identity (music); Set (abstract data type); Action (physics); Social identity theory; Business; Public relations; Mobile device; Sociology; Computer science; Social group; Political science; World Wide Web; Law; Telecommunications; Social science","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0008528791,0.00006504956,0.0001287005,0.00006298594,0.0004290952,0.00005581084,0.000772541,0.00002573834,1.230943e-7],"category_scores_gemma":[0.00009188408,0.00003790997,0.00006275358,0.001720337,0.003383198,0.001446615,0.0005528086,0.00006475485,1.131117e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002106478,"about_ca_system_score_gemma":0.00003704333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003126392,"about_ca_topic_score_gemma":0.000001014753,"domain_scores_codex":[0.9991872,5.621749e-7,0.0002305867,0.0001020581,0.0002848226,0.0001947648],"domain_scores_gemma":[0.9991665,0.00004509003,0.00035463,0.0001201518,0.0002942659,0.00001929478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005742371,0.00001451803,0.002248222,0.00006729407,0.00002071527,2.166356e-9,0.01247692,8.146657e-7,0.01902043,0.8213463,0.0008739355,0.1439251],"study_design_scores_gemma":[0.0008356064,0.0005644743,0.02088604,0.00007414017,0.00004562752,0.00002277299,0.08027083,0.001965759,0.7237841,0.01430884,0.1568938,0.000347939],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915022,0.00005389869,0.0009995028,0.003096252,0.00006147278,0.0003976531,0.000003953088,0.00004689878,0.003838169],"genre_scores_gemma":[0.9954804,0.0000665686,0.003332289,0.001088166,0.000004557806,0.00001855505,6.608573e-8,0.000001190212,0.000008206674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8070375,"threshold_uncertainty_score":0.999329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009113489498188432,"score_gpt":0.2511000490977823,"score_spread":0.2419865595995939,"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."}}