{"id":"W4321770418","doi":"10.18235/0004754","title":"The Causal Impact of Covid-19 Government-backed Loans on MSMEs Liquidity and Earnings","year":2023,"lang":"en","type":"report","venue":"","topic":"Business, Innovation, and Economy","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Loan; Market liquidity; Business; Government (linguistics); Earnings; Shock (circulatory); Coronavirus disease 2019 (COVID-19); Financial system; Finance; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01291902,0.0009965118,0.002076039,0.0004773715,0.0005225367,0.001122289,0.0008016377,0.002408394,0.01107184],"category_scores_gemma":[0.03324736,0.0004538441,0.002206275,0.0005103516,0.001444184,0.000945903,0.000762161,0.002632405,0.0005551134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001159361,"about_ca_system_score_gemma":0.002526083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001824655,"about_ca_topic_score_gemma":0.002113994,"domain_scores_codex":[0.9893454,0.008493839,0.0005158578,0.0007890054,0.0003786415,0.0004773003],"domain_scores_gemma":[0.9691203,0.02313999,0.004398991,0.001236325,0.0009433741,0.001161064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"observational","study_design_scores_codex":[0.8822649,0.02130132,0.009292322,0.01297517,0.008265495,0.0001732442,0.0003565957,0.001941288,0.00206185,0.002945571,0.003299221,0.05512303],"study_design_scores_gemma":[0.6727405,0.2732902,0.01812898,0.001783965,0.01342351,0.0001076413,0.0003194124,0.003449097,0.002896902,0.004622777,0.009149143,0.00008787856],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9277891,0.01904153,0.004163829,0.005147513,0.002027048,0.02839534,0.003701282,0.0001736551,0.009560696],"genre_scores_gemma":[0.9633116,0.002465815,0.005380804,0.001168588,0.0003879758,0.02436125,0.0005456618,0.00001474665,0.002363604],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01291902,"threshold_uncertainty_score":0.06832314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1308002427550408,"score_gpt":0.3217019618114176,"score_spread":0.1909017190563769,"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."}}