{"id":"W2333295261","doi":"10.1109/msp.2016.22","title":"Security for the High-Risk User: Separate and Unequal","year":2016,"lang":"en","type":"article","venue":"IEEE Security & Privacy","topic":"Financial Literacy, Pension, Retirement Analysis","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Internet privacy; Commodity; Computer security; Business; Computer science; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008822537,0.0003218256,0.0003789733,0.000162396,0.0006143166,0.0003642156,0.0005488409,0.0001183925,0.0002146927],"category_scores_gemma":[0.0005314726,0.0001964704,0.000205875,0.0004257034,0.000157618,0.001304997,0.0003268808,0.0001672302,0.0002670513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003905009,"about_ca_system_score_gemma":0.0000328985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001878559,"about_ca_topic_score_gemma":0.0007500054,"domain_scores_codex":[0.9980625,0.0000379962,0.0004450041,0.0005616018,0.000376034,0.00051687],"domain_scores_gemma":[0.9981917,0.0003906562,0.0004055881,0.0006691403,0.0003118274,0.00003111667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007704053,0.0004192723,0.6772189,0.0005044717,0.0003048948,0.00002523968,0.002545617,0.00002756089,0.001931343,0.1362008,0.1591661,0.02088545],"study_design_scores_gemma":[0.003109933,0.00005985121,0.08681871,0.0001547187,0.0008302368,0.000001757795,0.0001197866,0.004839138,0.001347847,0.1621558,0.7396161,0.0009461636],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953713,0.000298889,0.0005644646,0.001953562,0.0007314729,0.0006724837,0.00005491055,0.0001427168,0.0002101756],"genre_scores_gemma":[0.9964345,0.0002391758,0.00009470574,0.000934249,0.001863595,0.00006220031,0.00002181919,0.00004170157,0.0003081094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5904002,"threshold_uncertainty_score":0.8011829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01270277565297562,"score_gpt":0.2365301167250588,"score_spread":0.2238273410720832,"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."}}