{"id":"W4407582857","doi":"10.1016/j.jeconom.2025.105970","title":"Machine Learning for Economic Policy","year":2025,"lang":"en","type":"article","venue":"Journal of Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bank of Canada","funders":"","keywords":"Econometrics; Economics; Computer science; Artificial intelligence; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.00159837,0.0002000277,0.0008242094,0.003837015,0.0001423385,0.0001330048,0.0004495141,0.0001398144,0.0004913343],"category_scores_gemma":[0.0009946774,0.0002312588,0.0004840124,0.0004536035,0.00004689818,0.0005281367,0.00005824474,0.0002973464,0.0002710837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006680949,"about_ca_system_score_gemma":0.0001349155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002398678,"about_ca_topic_score_gemma":0.00001004144,"domain_scores_codex":[0.9976527,0.00001572013,0.00163864,0.000263934,0.00001702004,0.0004119816],"domain_scores_gemma":[0.9977535,0.0003952157,0.001426152,0.0002410474,0.0000266067,0.0001574694],"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":[0.0002181622,0.0001778877,0.2262238,0.0001431558,0.000848762,0.000004140813,0.0002491034,0.06293462,0.000006091031,0.6792259,0.01620938,0.01375905],"study_design_scores_gemma":[0.002346059,0.0003923224,0.01146502,0.00001950469,0.00002930805,0.00002992285,0.00004839139,0.05128533,0.00007045421,0.1354997,0.7984734,0.0003404966],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5559776,0.04502354,0.109101,0.01925685,0.009255932,0.001156786,0.001161343,0.00009736038,0.2589697],"genre_scores_gemma":[0.9896526,0.001684536,0.001981868,0.0008380845,0.0007635878,0.0000069302,0.00001154362,0.0000288947,0.005031972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7822641,"threshold_uncertainty_score":0.9430462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.203004382184511,"score_gpt":0.267525994313105,"score_spread":0.06452161212859403,"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."}}