{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001068056,0.000186183,0.0002689063,0.001757295,0.006587943,0.003665044,0.0004482096,0.0004507573,0.002081437],"category_scores_gemma":[0.002077793,0.0001599539,0.0001883808,0.001098337,0.003628397,0.001609671,0.003698579,0.0008134076,0.0002514954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001555578,"about_ca_system_score_gemma":0.00127623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008202909,"about_ca_topic_score_gemma":0.005924463,"domain_scores_codex":[0.9979587,0.0008523525,0.00009515433,0.0001910055,0.0003266764,0.0005761382],"domain_scores_gemma":[0.998235,0.0003521452,0.0005305813,0.0001300806,0.0001998063,0.0005523559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001415399,0.0001519653,0.3653869,0.00009234273,0.00004422548,0.002160836,0.5667271,0.000196037,0.003579162,0.02285087,0.001759972,0.03690902],"study_design_scores_gemma":[0.000007086182,0.0001089389,0.2816463,0.0001185082,0.00002265261,0.001258155,0.6831743,0.0005398467,0.0009152666,0.00331139,0.02885658,0.00004096909],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945937,0.0001234538,0.0001097146,0.0003086008,0.00001342001,0.000004525991,0.000007349043,0.0000029969,0.004836417],"genre_scores_gemma":[0.9992823,0.00008220333,0.00002675313,0.00004336023,0.000004406336,0.000002674914,0.000006375975,0.000002276205,0.0005496445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008202909,"threshold_uncertainty_score":0.01631033,"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."}}