{"id":"W4205385348","doi":"10.17762/de.vol2022iss1.8685","title":"Cyberspace and Women- Dimensions of Cybercrime against Women in India","year":2022,"lang":"en","type":"article","venue":"Design Engineering","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cybercrime; Stalking; Cyberspace; Legislation; Anonymity; Internet privacy; Hacker; Government (linguistics); The Internet; Political science; Law; Criminology; Computer security; Sociology; Computer science","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":[0.0009635367,0.0003830993,0.0003805397,0.002084419,0.009424299,0.005388113,0.00109761,0.0009461471,0.005082303],"category_scores_gemma":[0.002515976,0.0005183857,0.0004372592,0.003136207,0.006623398,0.002374046,0.004561896,0.002313803,0.0003640812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005318316,"about_ca_system_score_gemma":0.006333833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04750932,"about_ca_topic_score_gemma":0.06056719,"domain_scores_codex":[0.9984107,0.0005048591,0.00007083275,0.0001220346,0.0003074494,0.0005840961],"domain_scores_gemma":[0.9977098,0.000710686,0.000649904,0.0001027624,0.0002196202,0.0006072686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.000118469,0.0002568375,0.3083309,0.000474441,0.00006429478,0.003175522,0.6215142,0.0001308599,0.0006686563,0.02840407,0.006036424,0.03082527],"study_design_scores_gemma":[0.000007484913,0.00009990721,0.1877095,0.0003982956,0.00005848057,0.001563241,0.7780457,0.0001203388,0.0001965592,0.001953033,0.02979693,0.00005063167],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9595534,0.002456828,0.0001375274,0.008402863,0.000112997,0.00003423494,0.0001148501,0.00001597115,0.0291714],"genre_scores_gemma":[0.9963624,0.0009405175,0.00004713992,0.0006102989,0.00002176169,0.00001281574,0.00002323957,0.000004380604,0.001977537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04750932,"threshold_uncertainty_score":0.09446555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01010699926691865,"score_gpt":0.1963656254006087,"score_spread":0.18625862613369,"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."}}