{"id":"W2895821261","doi":"10.3968/10582","title":"Research on Chinese Personal Information Protection Legislation in the Era of Big Data","year":2018,"lang":"en","type":"article","venue":"Canadian social science","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Legislation; Transparency (behavior); Data Protection Act 1998; Accountability; Situational ethics; Personally identifiable information; General Data Protection Regulation; Big data; Information processing; Internet privacy; Data processing; Business; Computer science; Law; Computer security; Political science; Psychology; Data mining; Database","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.008242475,0.00005080078,0.00005757523,0.0003794389,0.002639344,0.0002365664,0.001376911,0.00008165129,0.00002917858],"category_scores_gemma":[0.002462708,0.0000416282,0.0000128875,0.003372287,0.001897453,0.001862288,0.0001097548,0.00033884,0.00004431112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005528103,"about_ca_system_score_gemma":0.00202356,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4076914,"about_ca_topic_score_gemma":0.6216988,"domain_scores_codex":[0.9978934,0.0003461861,0.0001406561,0.0001888946,0.001036387,0.0003944977],"domain_scores_gemma":[0.9991252,0.00005545856,0.0000662002,0.0002899239,0.0003530569,0.0001102002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006342889,0.00006539124,0.008500338,0.00001799024,0.00000374866,0.000001717806,0.3282188,7.74428e-7,0.0003524933,0.08733279,0.007475255,0.5679672],"study_design_scores_gemma":[0.0006506583,0.0004268584,0.3298617,0.0000802545,0.000006103568,0.000002528228,0.1490004,0.003128249,0.0001374471,0.02403418,0.4922301,0.0004414877],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5983643,0.00002003369,0.0004889246,0.01950182,0.001419147,0.001544961,0.0001834545,0.00003259784,0.3784448],"genre_scores_gemma":[0.9986017,0.000006520762,0.00002039926,0.0002443907,0.001074044,0.00001866239,0.00001985229,0.000001766837,0.00001262593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5675257,"threshold_uncertainty_score":0.9986591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1406300173761579,"score_gpt":0.3918251673283525,"score_spread":0.2511951499521946,"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."}}