{"id":"W3210034303","doi":"","title":"G-multipliers in Canada: How large? And Why?","year":2021,"lang":"en","type":"article","venue":"Cahiers de recherche","topic":"Fiscal Policy and Economic Growth","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economics; Shock (circulatory); Government spending; Production (economics); Government (linguistics); Autoregressive model; Exchange rate; Capital (architecture); Public good; Public spending; Monetary economics; Econometrics; Macroeconomics; Microeconomics; Market economy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006647953,0.0004169937,0.0004033835,0.00194225,0.001047292,0.001633606,0.0005062594,0.0003389158,0.002178047],"category_scores_gemma":[0.003888054,0.0001611464,0.0003708054,0.003095633,0.0007809961,0.0004763136,0.0007264886,0.000847825,0.0001558594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02275818,"about_ca_system_score_gemma":0.034556,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9939502,"about_ca_topic_score_gemma":0.9949933,"domain_scores_codex":[0.9996278,0.00003680972,0.00001180801,0.00006065194,0.00009434213,0.0001685625],"domain_scores_gemma":[0.9985737,0.0002737278,0.0003133806,0.00004020248,0.0005446422,0.0002543704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003295036,0.00009386396,0.8224493,0.0002963598,0.0003458043,0.0006490498,0.0009731677,0.04531389,0.0009051813,0.03695966,0.03356001,0.05812418],"study_design_scores_gemma":[0.0001086847,0.00006036457,0.9084364,0.0001782432,0.0001798513,0.0001412949,0.002786598,0.04670781,0.001479386,0.008353924,0.03148232,0.00008518682],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612429,0.003360906,0.001803296,0.006238323,0.00005454613,0.00005617401,0.01222708,0.0001429725,0.01487379],"genre_scores_gemma":[0.9900788,0.001305555,0.001407768,0.0001873932,0.00001960291,0.00001400881,0.003735759,0.00001923368,0.003231904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02275818,"threshold_uncertainty_score":0.1651229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1235483776930403,"score_gpt":0.2615624272846336,"score_spread":0.1380140495915934,"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."}}