{"id":"W2585968287","doi":"","title":"Big Brother’s shadow: Decline in reported use of electronic surveillance by Canadian Federal Law Enforcement","year":2013,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"European Criminal Justice and Data Protection","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Law enforcement; Government (linguistics); Shadow (psychology); Law; Legislation; Politics; Enforcement; Political science; Electronic surveillance; Federal election; Public administration","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003522997,0.0002679854,0.0003645211,0.003875783,0.004185031,0.003573649,0.003039465,0.0009941236,0.003792285],"category_scores_gemma":[0.0232515,0.000340834,0.0003119938,0.007516964,0.003351148,0.001561823,0.002053313,0.001482788,0.0002752647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05873975,"about_ca_system_score_gemma":0.06717187,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.997018,"about_ca_topic_score_gemma":0.9978782,"domain_scores_codex":[0.9949023,0.0004043334,0.0002204609,0.0005851653,0.002798897,0.001088877],"domain_scores_gemma":[0.9797419,0.002233915,0.004330942,0.0009566651,0.01053576,0.002200809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001953912,0.00009872638,0.7831927,0.000289046,0.00008533365,0.000296001,0.03852973,0.0003970695,0.0006513587,0.006241393,0.03394924,0.136074],"study_design_scores_gemma":[0.00000391632,0.00002330176,0.967751,0.0001511635,0.00001570547,0.0000667886,0.01555101,0.0004299846,0.000263206,0.0001565776,0.01555093,0.00003642428],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9358218,0.003757481,0.0006311115,0.02705121,0.0001067078,0.00006184565,0.006358637,0.00009283955,0.02611846],"genre_scores_gemma":[0.9940649,0.001011743,0.0002193594,0.001051453,0.0000176479,0.000009706589,0.0008567396,0.00001544758,0.002753076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05873975,"threshold_uncertainty_score":0.4261887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03397504766286177,"score_gpt":0.2758595922204039,"score_spread":0.2418845445575421,"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."}}