{"id":"W2143869283","doi":"10.1093/cybsec/tyv009","title":"Keys under doormats: mandating insecurity by requiring government access to all data and communications","year":2015,"lang":"en","type":"article","venue":"Journal of Cybersecurity","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":152,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"William and Flora Hewlett Foundation; Ford Foundation","keywords":"Law enforcement; Mandate; The Internet; Enforcement; Internet privacy; Computer security; Government (linguistics); Business; Secrecy; Law; Computer science; Political science; World Wide Web","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":[0.00438926,0.0001248367,0.0002363631,0.00006119427,0.0004609504,0.0004859897,0.002759255,0.0001035018,0.00002559756],"category_scores_gemma":[0.002117084,0.0001215519,0.00003800068,0.0002274795,0.0002116431,0.003353144,0.003043687,0.0004671753,0.000007839732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004050133,"about_ca_system_score_gemma":0.00022388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00277857,"about_ca_topic_score_gemma":0.004728237,"domain_scores_codex":[0.9975752,0.0004705628,0.0005099335,0.0002051073,0.0009586697,0.0002804795],"domain_scores_gemma":[0.9976547,0.0001517606,0.0004862632,0.0009732157,0.0002297451,0.0005042761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004559851,0.001358784,0.02192059,0.000117274,0.0003734166,0.00003126853,0.1651058,0.0000185186,0.0007080876,0.02447967,0.7560856,0.02934508],"study_design_scores_gemma":[0.001235917,0.0001685588,0.002210305,0.0001338364,0.00009455875,0.00003501004,0.02409959,0.0004420032,0.0002062211,0.02798023,0.9430368,0.0003569803],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8844731,0.004768927,0.01432307,0.07178393,0.001630541,0.001043392,0.0007574023,0.0001117821,0.02110781],"genre_scores_gemma":[0.9932157,0.001433555,0.003734467,0.001255773,0.0002989129,0.000003758531,0.00002844954,0.00001035879,0.00001897555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1869512,"threshold_uncertainty_score":0.5127428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1753037543284046,"score_gpt":0.4102463819164525,"score_spread":0.2349426275880479,"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."}}