{"id":"W2769084577","doi":"10.6000/1929-4409.2017.06.23","title":"What Happens When Investigating A Crime Takes Up Too Much Time? An Examination of How Optimal Law Enforcement Theory Impacts Sentencing","year":2017,"lang":"en","type":"article","venue":"International Journal of Criminology and Sociology","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workload; Law enforcement; Criminology; Sentence; Enforcement; Criminal justice; Psychology; Economic Justice; Law; Political science; Computer security; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0098691,0.0002350007,0.0003537248,0.001832066,0.002219989,0.002679738,0.0009979226,0.0005496267,0.002720511],"category_scores_gemma":[0.09169754,0.0003044417,0.0004707385,0.001881299,0.001627412,0.003373931,0.002680112,0.001319679,0.0002952818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002778532,"about_ca_system_score_gemma":0.003080109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01231335,"about_ca_topic_score_gemma":0.03281764,"domain_scores_codex":[0.9901381,0.005844285,0.0007477643,0.0008783286,0.001489858,0.0009018236],"domain_scores_gemma":[0.9351918,0.03629939,0.02015382,0.002216993,0.00334832,0.002789761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001715744,0.0002505171,0.9302051,0.0001838105,0.0001282122,0.0002795674,0.01134946,0.0007498538,0.0003081798,0.002493909,0.002880554,0.05099921],"study_design_scores_gemma":[0.000008219261,0.000125833,0.9679216,0.0003066663,0.0000582811,0.0002117204,0.02302292,0.001787734,0.0002707753,0.003108193,0.003146353,0.00003171645],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906613,0.0003905613,0.001153732,0.003760398,0.00003757387,0.00004167978,0.0003138147,0.00001320169,0.003627699],"genre_scores_gemma":[0.9981109,0.000255206,0.0009364337,0.0002084006,0.00002880308,0.0000318563,0.0001723856,0.00001053654,0.0002454248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01231335,"threshold_uncertainty_score":0.0521934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1395812645631383,"score_gpt":0.3991744624084232,"score_spread":0.2595931978452849,"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."}}