{"id":"W1707133303","doi":"10.1111/jpet.12115","title":"Profiling, Screening, and Criminal Recruitment","year":2014,"lang":"en","type":"article","venue":"Journal of Public Economic Theory","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Officer; Cartel; Law enforcement; Racial profiling; Enforcement; Terrorism; Population; Criminal law; Profiling (computer programming); Business; Law and economics; Economics; Law; Political science; Industrial organization; Computer science; Biology; Sociology","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":[0.004505954,0.001150885,0.001627642,0.001165289,0.001411962,0.002849247,0.001843285,0.003825427,0.009611359],"category_scores_gemma":[0.01869249,0.0007836307,0.0007672313,0.00143233,0.003148051,0.003636324,0.002071294,0.002368331,0.0009957312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003182712,"about_ca_system_score_gemma":0.002855032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01910249,"about_ca_topic_score_gemma":0.01692063,"domain_scores_codex":[0.9953966,0.002611338,0.0001252919,0.0005641666,0.0002851484,0.001017538],"domain_scores_gemma":[0.9882853,0.006775896,0.002787012,0.0005190451,0.0003654441,0.00126739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009029115,0.001210484,0.04520324,0.0001842217,0.0001298879,0.0006231801,0.001028307,0.4905234,0.0008020512,0.4034747,0.00706137,0.04885627],"study_design_scores_gemma":[0.0003984091,0.0006428871,0.01385161,0.0001049428,0.00009590621,0.0002903078,0.0006753693,0.7172298,0.0003749412,0.2605228,0.005702944,0.0001101033],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6925348,0.001148379,0.2134026,0.01574393,0.0002011977,0.001186112,0.001667592,0.0002767031,0.07383874],"genre_scores_gemma":[0.9725566,0.000433419,0.007191827,0.0004475517,0.00006831665,0.0003152231,0.0001533549,0.00001420892,0.01881951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01910249,"threshold_uncertainty_score":0.03798258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1542816451332984,"score_gpt":0.3829263031456091,"score_spread":0.2286446580123107,"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."}}