{"id":"W2316695912","doi":"10.1111/jels.12035","title":"Empirical Analysis of Data Breach Litigation","year":2014,"lang":"en","type":"article","venue":"Journal of Empirical Legal Studies","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":170,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Office; Deutscher Akademischer Austauschdienst; York University","keywords":"Plaintiff; Data breach; Redress; Class action; Business; Harm; Odds; Credit card; Securities fraud; Information privacy; Law; Actuarial science; Internet privacy; Payment; Finance; Political science; Supreme court","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.001397808,0.0001593846,0.0008321843,0.000325772,0.0001504903,0.0000607088,0.001191808,0.00004701271,0.00001031295],"category_scores_gemma":[0.0007811083,0.0001084683,0.0002864634,0.001244148,0.000159534,0.0009828659,0.0009463567,0.0002074168,0.000004658186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005525634,"about_ca_system_score_gemma":0.00005585101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005004963,"about_ca_topic_score_gemma":0.00006348253,"domain_scores_codex":[0.9976841,0.000174528,0.0009360852,0.0002732865,0.0007061808,0.0002258049],"domain_scores_gemma":[0.9974505,0.0007478861,0.0005055355,0.0006264606,0.0005727328,0.00009686415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002137421,0.00147867,0.4384211,0.0001995185,0.03202469,0.0000813496,0.01391528,0.002062944,0.0005199651,0.05133865,0.3601354,0.09960871],"study_design_scores_gemma":[0.001500002,0.001566203,0.4770114,0.0001284403,0.003945434,0.00004365619,0.0009373813,0.03306109,0.0006492386,0.004158526,0.4763944,0.0006042723],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4916258,0.008370325,0.4478777,0.03663247,0.001304919,0.0002265869,0.00003007391,0.00009231782,0.01383986],"genre_scores_gemma":[0.9906031,0.0002651152,0.00777363,0.0009691929,0.0002380374,0.000001064656,0.000004187998,0.000004877037,0.0001407345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4989774,"threshold_uncertainty_score":0.4423208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1744729292061837,"score_gpt":0.4349621365672465,"score_spread":0.2604892073610628,"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."}}