{"id":"W4402519388","doi":"10.1016/j.eap.2024.08.031","title":"Navigating post-Covid-19 economic recovery in WAEMU: A DSGE approach","year":2024,"lang":"en","type":"article","venue":"Economic Analysis and Policy","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Fundação de Apoio à Pesquisa do Estado da Paraíba; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Coronavirus disease 2019 (COVID-19); Dynamic stochastic general equilibrium; Economics; Economic recovery; Monetary economics; Keynesian economics; Medicine; Monetary policy; Internal medicine","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001322741,0.0003991218,0.001141732,0.001471381,0.0001674377,0.0004888393,0.0003306931,0.0002278862,0.001905019],"category_scores_gemma":[0.00009888058,0.0004734698,0.0006010458,0.000475286,0.0001213015,0.0007912624,0.0001280915,0.0004176686,0.002057706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0014591,"about_ca_system_score_gemma":0.0002177454,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08849052,"about_ca_topic_score_gemma":0.002511216,"domain_scores_codex":[0.9966511,0.00004926338,0.001406415,0.001156708,0.00001926087,0.000717236],"domain_scores_gemma":[0.9984471,0.0002027701,0.0003355564,0.0005605316,0.000002764817,0.0004512389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001273131,0.0001402513,0.215924,0.0004488371,0.005248038,0.00004110362,0.009549928,0.311455,0.00001208702,0.4291862,0.004101239,0.02376603],"study_design_scores_gemma":[0.00104229,0.0001229426,0.03445091,0.0000342789,0.0002273929,0.00006651261,0.0005208029,0.8475236,0.00001244817,0.05021642,0.06443308,0.001349353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9608809,0.004790922,0.00142818,0.005883541,0.0003313677,0.0002587109,0.001427938,0.0001028929,0.02489552],"genre_scores_gemma":[0.9932342,0.001555197,0.0004838694,0.00232026,0.0008113989,0.00004728683,0.0002351414,0.00005330878,0.00125929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5360686,"threshold_uncertainty_score":0.9997717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04564075850653761,"score_gpt":0.2896743181799205,"score_spread":0.2440335596733829,"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."}}