{"id":"W4318587330","doi":"10.2139/ssrn.4338235","title":"The Employment Effects of a Pandemic Wage Subsidy","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Employment and Welfare Studies","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Government of Canada; Statistics Canada; University of Toronto","funders":"","keywords":"Subsidy; Pandemic; Wage; Labour economics; Economics; Coronavirus disease 2019 (COVID-19); Business; 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":[0.002453956,0.0002402126,0.0004059376,0.000480621,0.001530681,0.002281555,0.0007829667,0.003771137,0.01898546],"category_scores_gemma":[0.007831661,0.0002090662,0.000554698,0.000467802,0.002090883,0.001433239,0.002156394,0.002745953,0.0007390614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002904973,"about_ca_system_score_gemma":0.003325853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02898651,"about_ca_topic_score_gemma":0.0373514,"domain_scores_codex":[0.9987401,0.0004713183,0.00003345883,0.0000658433,0.0001622891,0.0005269639],"domain_scores_gemma":[0.997218,0.001518316,0.0003607379,0.0001269268,0.0002390386,0.0005368867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.02849479,0.01237877,0.1448458,0.0008563445,0.0005098677,0.006548577,0.007167244,0.04048887,0.008769176,0.4378173,0.08181836,0.2303049],"study_design_scores_gemma":[0.004497482,0.01090837,0.6195585,0.0007351424,0.0006611427,0.0007312058,0.05838015,0.0226613,0.003495953,0.1753942,0.1026896,0.000287039],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8790649,0.001055008,0.0002418871,0.03626748,0.0008941611,0.00008089339,0.0004974991,0.00002122113,0.08187688],"genre_scores_gemma":[0.9902964,0.0002905068,0.00005372812,0.002063656,0.0001783977,0.00001452842,0.00003720992,0.000005119353,0.007060427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02898651,"threshold_uncertainty_score":0.06351268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02581427267391486,"score_gpt":0.3753185948290664,"score_spread":0.3495043221551515,"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."}}