{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009512174,0.0004821088,0.0007083475,0.01266008,0.003031932,0.004153947,0.002415197,0.002804935,0.009092324],"category_scores_gemma":[0.09628277,0.00054988,0.0006802761,0.01316581,0.002287716,0.003531076,0.002345288,0.003436101,0.001951995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003673106,"about_ca_system_score_gemma":0.002917981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01800409,"about_ca_topic_score_gemma":0.01586567,"domain_scores_codex":[0.9821761,0.005266493,0.002508396,0.001869745,0.006422546,0.001756652],"domain_scores_gemma":[0.7187517,0.1518392,0.1090329,0.005312671,0.01194409,0.003119455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002013582,0.001429961,0.9397686,0.000420599,0.0002233122,0.0009937208,0.003648673,0.002896625,0.0002707443,0.01107098,0.01321608,0.02585947],"study_design_scores_gemma":[0.00005049139,0.0003435756,0.9519185,0.0004859017,0.0001521962,0.001429011,0.01168016,0.0112713,0.001015416,0.00466378,0.01690004,0.0000895863],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9697903,0.002315831,0.00240065,0.002067482,0.00003205772,0.0003924988,0.006805987,0.00005716553,0.01613797],"genre_scores_gemma":[0.9873799,0.00106663,0.001020948,0.0004447217,0.00008486796,0.0003158624,0.006280805,0.00002174275,0.003384616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01800409,"threshold_uncertainty_score":0.05030578,"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."}}