{"id":"W6982438324","doi":"","title":"Improving public safety through technology: The past, present, and future of electronic monitoring in Canada","year":2017,"lang":"en","type":"article","venue":"Arca (British Columbia Electronic Library Network)","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Harm; Statutory law; Prison; Criminal justice; Position (finance); Economic Justice","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004141441,0.0001525419,0.0003625084,0.00003056313,0.002944113,0.0008838628,0.001009373,0.0001553139,0.001102125],"category_scores_gemma":[0.0000544394,0.0002023482,0.00006040981,0.0005775256,0.0005467128,0.001544457,0.0003852645,0.0006878647,5.220649e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004101007,"about_ca_system_score_gemma":0.003682949,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8841953,"about_ca_topic_score_gemma":0.9924872,"domain_scores_codex":[0.9970151,0.0002203316,0.0003971589,0.0004370813,0.0003956401,0.001534674],"domain_scores_gemma":[0.9987623,0.000231085,0.0003561669,0.0004991702,0.00006459974,0.00008667995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001466118,0.00003282831,0.7393147,0.00001811197,0.00010001,0.00001341597,0.0004769519,0.00001026884,5.126355e-7,0.1160411,0.02083677,0.1231407],"study_design_scores_gemma":[0.0008762306,0.0001164067,0.0584153,0.0001001458,0.0000354807,0.00001470173,0.003801057,0.0002132434,0.000007684967,0.2552161,0.6807799,0.0004237637],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.389631,0.1587424,0.0000207074,0.1129755,0.002768999,0.003311713,0.00006521786,0.0005370321,0.3319474],"genre_scores_gemma":[0.9768607,0.01808091,0.00005615516,0.000118318,0.003039239,0.00008039878,0.000006012186,0.00003062603,0.001727659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6808994,"threshold_uncertainty_score":0.999811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00874548720061336,"score_gpt":0.2135112543024014,"score_spread":0.2047657671017881,"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."}}