{"id":"W4386391510","doi":"10.1002/mde.3991","title":"Settlements in the presence of leniency programs: Costs and benefits","year":2023,"lang":"en","type":"article","venue":"Managerial and Decision Economics","topic":"Merger and Competition Analysis","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Carleton University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Cartel; Settlement (finance); Collusion; Conviction; Human settlement; Economics; Incentive; Price fixing; Microeconomics; Business; Law and economics; Public economics; Political science; Law; Finance; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005766572,0.0004951518,0.001083347,0.001505897,0.001696348,0.00338338,0.001726256,0.003045862,0.02032406],"category_scores_gemma":[0.0316529,0.0004703063,0.0004667702,0.0009521404,0.002286182,0.002578133,0.002717892,0.002876288,0.000978515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002092838,"about_ca_system_score_gemma":0.002642782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00252452,"about_ca_topic_score_gemma":0.00331721,"domain_scores_codex":[0.9940527,0.002638651,0.0002528903,0.0005048547,0.001054852,0.001496071],"domain_scores_gemma":[0.9555776,0.02489999,0.01121275,0.002267951,0.002548207,0.003493449],"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":[0.004214865,0.007767726,0.1090062,0.0009141048,0.0005382128,0.005225963,0.001405577,0.2159235,0.009595335,0.3304587,0.01603628,0.2989135],"study_design_scores_gemma":[0.001070392,0.009452019,0.1264957,0.0006051603,0.0006972629,0.002714875,0.007921543,0.4805406,0.009598112,0.3358009,0.02479565,0.0003076467],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.914547,0.001036447,0.02117012,0.003320309,0.0001515222,0.0004226457,0.0001756693,0.0002206782,0.05895561],"genre_scores_gemma":[0.9960428,0.00009995778,0.0009886614,0.00008912203,0.00005954617,0.00003202927,0.00002075871,0.000005734459,0.002661468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02032406,"threshold_uncertainty_score":0.06799072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03800226939949864,"score_gpt":0.2373353579315166,"score_spread":0.1993330885320179,"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."}}