{"id":"W2207893187","doi":"10.1017/9781108227223","title":"Advances in Economics and Econometrics","year":2017,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Variety (cybernetics); Eleventh; State (computer science); Financial econometrics; Econometric model; Regional science; Economics; Financial market; Sociology; Econometrics; Computer science; Finance","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003171094,0.000418405,0.001014729,0.0008875605,0.0002042426,0.0001592454,0.0007044786,0.0004764361,0.000009630426],"category_scores_gemma":[0.00003978912,0.0006328366,0.0001904423,0.00001151634,0.0002873146,0.0006950569,0.0003473928,0.0004520061,0.00009020042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006445267,"about_ca_system_score_gemma":0.00008341287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005573262,"about_ca_topic_score_gemma":0.00003446569,"domain_scores_codex":[0.9980863,0.00001487002,0.0005355519,0.0008648765,0.00001737322,0.0004810648],"domain_scores_gemma":[0.9979377,0.00008852505,0.0009184483,0.0008233364,0.00001004061,0.0002219536],"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":[0.00005209243,0.0000163367,0.0005803095,0.0001241765,0.00007809157,0.00004974347,0.00006440425,0.0001545067,3.494991e-8,0.9781024,0.01512509,0.005652859],"study_design_scores_gemma":[0.0008863534,0.0000432264,0.0005795786,0.00004812937,0.00001995545,0.00001072117,0.00001120085,0.001747512,0.000002104046,0.000754766,0.9952571,0.0006393672],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002326641,0.005625292,0.00005198354,0.0000380562,0.0004387089,0.0003224649,0.0015542,0.00003181756,0.9896109],"genre_scores_gemma":[0.005835047,0.03612959,0.0000738783,0.00008803895,0.000202873,0.000001711777,0.00007964135,0.00005308304,0.9575362],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.980132,"threshold_uncertainty_score":0.9996123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05852525055430257,"score_gpt":0.1966354124965465,"score_spread":0.1381101619422439,"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."}}