{"id":"W3125918342","doi":"10.1111/caje.12204","title":"Crime, apprehension and clearance rates: Panel data evidence from Canadian provinces","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Economics/Revue canadienne d économique","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Waterloo","funders":"","keywords":"Apprehension; Commit; Property crime; Panel data; Crime rate; Economics; Econometrics; Demographic economics; Psychology; Criminology; Violent crime; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.002185256,0.0005164372,0.0009894921,0.003438746,0.002683608,0.00174002,0.001582566,0.0004845256,0.004041208],"category_scores_gemma":[0.007568608,0.0004579336,0.001187849,0.01080554,0.0008889908,0.0004107152,0.0009961578,0.0008888816,0.0004340175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02413847,"about_ca_system_score_gemma":0.02780962,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9982765,"about_ca_topic_score_gemma":0.9982867,"domain_scores_codex":[0.9981647,0.0003434132,0.0001211211,0.0002457962,0.0005858789,0.0005390914],"domain_scores_gemma":[0.9907268,0.001521186,0.001522416,0.0007876454,0.004721359,0.0007206903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000266415,0.000109063,0.9631245,0.000175394,0.0005419561,0.0001997409,0.001768299,0.005580463,0.000240741,0.001855865,0.01267031,0.01346709],"study_design_scores_gemma":[0.00002455184,0.00001887083,0.9891912,0.00008090985,0.0001309731,0.00003257327,0.001574699,0.002348074,0.0001904676,0.0001983898,0.006162836,0.00004631569],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9450747,0.002198295,0.0007828449,0.001083356,0.00004232205,0.00008775792,0.04267595,0.0000422886,0.008012353],"genre_scores_gemma":[0.9792254,0.001155825,0.0005731017,0.00009820748,0.000008542995,0.00002774688,0.01681008,0.00001345384,0.002087684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02413847,"threshold_uncertainty_score":0.1751377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.391517801141552,"score_gpt":0.2798971485958585,"score_spread":0.1116206525456936,"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."}}