{"id":"W244177233","doi":"","title":"Nominal GDP Targeting: A Simple Rule to Improve Fed Performance","year":2014,"lang":"en","type":"article","venue":"Cato Journal","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economics; Monetary policy; Monetarism; Keynesian economics; Gold standard (test); Variety (cybernetics); Monetary economics; Market liquidity; Value (mathematics); Macroeconomics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001121136,0.0001843915,0.0003960161,0.0002385185,0.0003085445,0.0001941043,0.0003413971,0.00008323653,0.001228197],"category_scores_gemma":[0.000177652,0.0002009161,0.0001486553,0.00009896421,0.00003277258,0.0003700583,0.000061645,0.0003162082,0.003861809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001501259,"about_ca_system_score_gemma":0.00002328366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001355697,"about_ca_topic_score_gemma":0.000005064453,"domain_scores_codex":[0.998361,0.00001697178,0.0007077518,0.0002998279,0.00003121537,0.0005832269],"domain_scores_gemma":[0.9988928,0.00003491918,0.0003639734,0.0002756306,0.00001700123,0.0004157127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000695138,0.0004097253,0.4174586,0.0001969834,0.0004472215,0.00002699715,0.006621177,0.01784975,0.002171753,0.02100228,0.4757787,0.05734164],"study_design_scores_gemma":[0.001329999,0.0005715221,0.04567387,0.00001715257,0.0000111664,0.0001503104,0.00008772236,0.05350643,0.0009591531,0.01126697,0.8857563,0.0006694641],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98228,0.000229414,0.003852151,0.001334902,0.001128598,0.000105328,0.00006069984,0.00001975656,0.01098911],"genre_scores_gemma":[0.9926476,0.00004186335,0.00119619,0.001999707,0.001237598,0.000007963588,0.000008182036,0.00002967077,0.002831166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4099775,"threshold_uncertainty_score":0.9996848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03471560909273354,"score_gpt":0.2163160143030341,"score_spread":0.1816004052103006,"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."}}