{"id":"W2081131628","doi":"10.1007/s10940-007-9028-0","title":"Community Variation in Crime Clearance: A Multilevel Analysis with Comments on Assessing Police Performance","year":2007,"lang":"en","type":"article","venue":"Journal of Quantitative Criminology","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":110,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Western University","funders":"","keywords":"Criminology; Clearance; Context (archaeology); Deterrence (psychology); Multilevel model; Workload; Poverty; Sample (material); Variation (astronomy); Psychology; Political science; Geography; Law; Computer science; Medicine","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":[0.01330067,0.0001825032,0.0005132821,0.002322429,0.001959556,0.001088072,0.0007532434,0.0008896188,0.002021488],"category_scores_gemma":[0.09124235,0.0002875644,0.00109175,0.004040241,0.0005887154,0.000970355,0.002214105,0.001079615,0.000244984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00109541,"about_ca_system_score_gemma":0.001579301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0294583,"about_ca_topic_score_gemma":0.09185623,"domain_scores_codex":[0.9828345,0.01187063,0.0009337094,0.001107316,0.002405536,0.0008483391],"domain_scores_gemma":[0.8939292,0.06698257,0.01200326,0.01047776,0.01493025,0.001676929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003511603,0.00009024952,0.9484084,0.0001165597,0.0004982615,0.000145634,0.01633659,0.0008265685,0.0008777966,0.0009192778,0.002859964,0.02856955],"study_design_scores_gemma":[0.000009170813,0.0001690946,0.982565,0.00006400712,0.0001822406,0.00006908455,0.01143764,0.002446985,0.0006644909,0.0004896909,0.001865351,0.00003730544],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932849,0.0001379681,0.002459718,0.0006048295,0.0000235045,0.00006603465,0.001371648,0.00001634363,0.002034966],"genre_scores_gemma":[0.99721,0.00003743612,0.001821203,0.00005565791,0.00001460555,0.00006263716,0.0004278806,0.00001296823,0.0003577023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0294583,"threshold_uncertainty_score":0.07034153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3227888063879401,"score_gpt":0.4858464801355388,"score_spread":0.1630576737475988,"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."}}