{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01471634,0.0005018688,0.000405901,0.001229462,0.00570469,0.008680474,0.002365908,0.004875431,0.01126935],"category_scores_gemma":[0.04734453,0.0008479054,0.0006995435,0.0009534695,0.01359697,0.0172194,0.01041976,0.007025121,0.003004747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003042743,"about_ca_system_score_gemma":0.008423035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007886938,"about_ca_topic_score_gemma":0.008438275,"domain_scores_codex":[0.9846811,0.006395982,0.0008673605,0.002280222,0.003398138,0.002377131],"domain_scores_gemma":[0.9631826,0.01464252,0.004137392,0.01102034,0.004537078,0.002479909],"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":[0.0001478678,0.0001284008,0.009458588,0.0001975536,0.00003943249,0.0004480015,0.00918137,0.001571278,0.003174181,0.8894542,0.03826303,0.04793614],"study_design_scores_gemma":[0.0001278773,0.0003123339,0.007904318,0.0008432474,0.0001440488,0.0008372252,0.01223815,0.005366848,0.01548034,0.440104,0.5163742,0.0002674256],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2901981,0.003549748,0.1980367,0.09471343,0.002408412,0.0004722255,0.0005038797,0.003355884,0.4067616],"genre_scores_gemma":[0.9334062,0.001204811,0.02114882,0.01720166,0.000250467,0.0003169556,0.0001543062,0.0003804534,0.02593637],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01471634,"threshold_uncertainty_score":0.07782841,"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."}}