{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005412557,0.000328045,0.0004378254,0.001994555,0.00819734,0.009462511,0.001016678,0.002260335,0.008557587],"category_scores_gemma":[0.01886153,0.0003198833,0.0004091033,0.0009239871,0.01557993,0.01292769,0.01692725,0.00532789,0.001071889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003057726,"about_ca_system_score_gemma":0.002373954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003912474,"about_ca_topic_score_gemma":0.005960179,"domain_scores_codex":[0.9922583,0.003015138,0.0002465448,0.0008724251,0.001675675,0.001931954],"domain_scores_gemma":[0.9918331,0.001631433,0.0008153955,0.001260159,0.0008584642,0.00360142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002824467,0.0003635161,0.07859657,0.00005956159,0.0001079974,0.0006786221,0.04931892,0.0003069268,0.001910785,0.7018755,0.01062565,0.1558735],"study_design_scores_gemma":[0.00005746723,0.0002127412,0.08758515,0.0004952931,0.0001133573,0.002156929,0.0886813,0.003550964,0.001424735,0.7536613,0.0619242,0.0001365468],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6623392,0.001855228,0.017019,0.06216139,0.000286699,0.0001155539,0.0001091898,0.00006041747,0.2560533],"genre_scores_gemma":[0.9926792,0.0002261745,0.0006868838,0.002565809,0.00006624837,0.00002467017,0.00001708076,0.00001346819,0.00372041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009462511,"threshold_uncertainty_score":0.02862799,"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."}}