{"id":"W3121752429","doi":"","title":"Deterring Repeat Offenders with Escalating Penalty Schedules: A Bayesian Approach","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Bayesian probability; Deterrence (psychology); Economics; Computer science; Econometrics; Computer security; Artificial intelligence; Law and economics","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":[0.007002751,0.001622562,0.003622843,0.002371787,0.000772483,0.002052971,0.003878251,0.003396732,0.005267972],"category_scores_gemma":[0.02581962,0.001550478,0.001307254,0.001337734,0.00127556,0.003197733,0.002451402,0.003512261,0.0006883744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001477714,"about_ca_system_score_gemma":0.002828574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005520368,"about_ca_topic_score_gemma":0.009322458,"domain_scores_codex":[0.9958233,0.002310066,0.0001751933,0.0005214621,0.0007255577,0.0004444485],"domain_scores_gemma":[0.9850541,0.01165388,0.00127825,0.0004277096,0.001065332,0.0005208873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002874754,0.0004172598,0.002518346,0.0001726897,0.0001933684,0.0001548212,0.0001299533,0.8846564,0.0004633715,0.03430128,0.00293255,0.07377254],"study_design_scores_gemma":[0.00004643722,0.0001638422,0.0006184459,0.00005520739,0.00006300098,0.00006671584,0.00004909208,0.9676191,0.0001172332,0.03058905,0.0005855404,0.00002641249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05255691,0.000717563,0.9355241,0.002257721,0.00008252364,0.0003906652,0.0002306954,0.0002421894,0.007997562],"genre_scores_gemma":[0.7877319,0.001249757,0.1985531,0.0006294355,0.0002792336,0.0005941154,0.00032379,0.00006739664,0.01057129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007002751,"threshold_uncertainty_score":0.03703457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0722583838536898,"score_gpt":0.3245440088518188,"score_spread":0.252285624998129,"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."}}