{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004991747,0.00005519231,0.000133247,0.0001166538,0.0001777619,0.0001503586,0.0001847515,0.00003878527,0.002092648],"category_scores_gemma":[0.0002475068,0.00004868293,0.000114296,0.0000189635,0.0001519562,0.0003536337,0.00003345653,0.0001148926,0.00001891206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008197978,"about_ca_system_score_gemma":0.00008836403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000585907,"about_ca_topic_score_gemma":0.0001391316,"domain_scores_codex":[0.9990053,0.000370611,0.0003100143,0.00007945086,0.00007355546,0.0001611306],"domain_scores_gemma":[0.9992612,0.0001437731,0.0003052592,0.00007676883,0.00005949483,0.0001535179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001445479,0.00004524615,0.01206961,0.000008039612,0.00005743653,0.000001114834,0.002118493,0.000002562293,0.0000334165,0.8313896,0.003571671,0.1506883],"study_design_scores_gemma":[0.0008696197,0.0004158025,0.01090308,0.00007544025,0.00008183941,0.00004722137,0.01906272,0.0001261719,0.0001801639,0.06339591,0.9046394,0.0002026557],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9293438,0.0003770239,0.01451533,0.002887511,0.00073362,0.0001139516,0.000003466173,0.00001211288,0.05201315],"genre_scores_gemma":[0.9960102,0.00007888713,0.001025153,0.0001514014,0.0006876588,0.000002933187,5.324219e-7,0.000006178171,0.002037062],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9010677,"threshold_uncertainty_score":0.9988196,"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."}}