{"id":"W2061471985","doi":"10.5539/ass.v10n18p158","title":"Subculture of Hackers in Russia","year":2014,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subculture (biology); Hacker; Popularity; Ideology; Sociology; Law; Political science; Computer security; Computer science; Politics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005218361,0.00006322804,0.0001140395,0.00006822047,0.0002360408,0.00004776566,0.000796967,0.0000263811,0.000006494255],"category_scores_gemma":[0.00004766987,0.00005279062,0.00003453064,0.001013996,0.0003998386,0.000415798,0.0002137266,0.00006258322,0.00001377075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000390008,"about_ca_system_score_gemma":0.00005262759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001445466,"about_ca_topic_score_gemma":0.00007289465,"domain_scores_codex":[0.9990146,0.00002391193,0.0001403276,0.000220884,0.0003460448,0.0002542128],"domain_scores_gemma":[0.9996934,0.00001386837,0.00005107897,0.000159502,0.00004275334,0.00003941463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[9.923004e-7,0.00001750795,0.001545943,0.000003448973,0.00000151693,5.935539e-7,0.006515034,5.023527e-7,0.001436483,0.8788161,0.0004805297,0.1111813],"study_design_scores_gemma":[0.0005222441,0.0001021233,0.9462014,0.00002702699,0.000004198505,0.000001584945,0.001356091,0.0004842466,0.006168791,0.01894138,0.02587269,0.0003182311],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02364055,0.00001790933,0.01079125,0.002112875,0.0002192207,0.00009415503,4.883858e-7,0.00004588401,0.9630777],"genre_scores_gemma":[0.9984365,0.000001977705,0.001177512,0.0002025121,0.00003868221,0.000002865197,1.666804e-7,0.000001440449,0.0001383803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9747959,"threshold_uncertainty_score":0.2152739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009302675265096576,"score_gpt":0.2567219206149688,"score_spread":0.2474192453498723,"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."}}