{"id":"W4235266065","doi":"10.4018/978-1-5225-0983-7.ch066","title":"Veillance","year":2016,"lang":"en","type":"book-chapter","venue":"Biometrics","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Hypocrisy; Computer security; Political science; Computer science; Internet privacy; Law","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":[0.0003289522,0.0008429347,0.0004036516,0.001745747,0.001287748,0.005221037,0.001245487,0.001813259,0.1837738],"category_scores_gemma":[0.00132566,0.0003050068,0.0004682537,0.001173772,0.0008378697,0.005026362,0.002751705,0.001972188,0.0894068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001382485,"about_ca_system_score_gemma":0.0007808286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002072384,"about_ca_topic_score_gemma":0.003608247,"domain_scores_codex":[0.9995112,0.00006678479,0.00002239213,0.00009784929,0.0002539863,0.00004764861],"domain_scores_gemma":[0.9996803,0.0000805315,0.00001611404,0.00006492325,0.0001078167,0.00005039504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003175327,0.00002968929,0.0001551612,0.0003341889,0.000005236354,0.0001849196,0.000893609,0.0003847797,0.001071251,0.2219867,0.4459604,0.3289623],"study_design_scores_gemma":[7.412502e-7,0.000003382148,0.00004251007,0.00005176577,6.454505e-7,0.0001154854,0.0000479902,0.00004551627,0.00008303201,0.002857101,0.9967492,0.000002661657],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.00062449,0.00749641,0.007047375,0.001511265,0.0022174,0.00005094263,0.0004665909,0.001090971,0.9794947],"genre_scores_gemma":[0.007126268,0.007041394,0.003731397,0.001049227,0.0006384249,0.00005146566,0.0007873063,0.0006076004,0.9789668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1837738,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0560344253582679,"score_gpt":0.3067235366788441,"score_spread":0.2506891113205762,"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."}}