{"id":"W3123269018","doi":"10.3386/w25095","title":"Optimal Law Enforcement with Ordered Leniency","year":2018,"lang":"en","type":"preprint","venue":"National Bureau of Economic Research","topic":"Law, Economics, and Judicial Systems","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Science Foundation","keywords":"Law enforcement; Enforcement; Law; Law and economics; Business; Economics; Political science","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004427828,0.0003289549,0.00113256,0.0008405625,0.0002828532,0.0002557525,0.001169723,0.0005348228,0.002822525],"category_scores_gemma":[0.0001563989,0.0004339837,0.0002711866,0.0001513659,0.001174712,0.0003201107,0.0007420743,0.0007496462,0.002030375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001716817,"about_ca_system_score_gemma":0.0008028426,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01515797,"about_ca_topic_score_gemma":0.001141105,"domain_scores_codex":[0.9958458,0.00007729957,0.001826966,0.001246702,0.0002607447,0.0007424991],"domain_scores_gemma":[0.9970083,0.0002661845,0.001116402,0.0007912577,0.0006111061,0.00020672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001256827,0.0001110095,0.002101554,0.0001371702,0.0003233783,0.000001578291,0.0002773853,0.006013193,0.00000296904,0.9845077,0.006358564,0.00003984873],"study_design_scores_gemma":[0.0010771,0.0003682525,0.000233059,0.0001135117,0.000007802624,0.00000542908,0.0001013402,0.005772691,0.0001614951,0.9630155,0.02860793,0.0005359119],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04752696,0.0007852779,0.0002456693,0.0005522688,0.001177018,0.001138561,0.0006779262,0.0000361073,0.9478602],"genre_scores_gemma":[0.994281,0.0002971652,0.001040654,0.00008882459,0.001575715,0.0002546105,0.0003837653,0.0000784103,0.001999843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.946754,"threshold_uncertainty_score":0.9998112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2448067998622251,"score_gpt":0.4093579367259377,"score_spread":0.1645511368637125,"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."}}