{"id":"W4310988721","doi":"10.3390/jrfm15120559","title":"Employee Compensation, Training and Financial Performance during the COVID-19 Pandemic","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Compensation (psychology); Financial compensation; Human resources; Financial crisis; Business; Human capital; Robustness (evolution); Order (exchange); Coronavirus disease 2019 (COVID-19); Compensation of employees; Pandemic; Sample (material); Process (computing); Accounting; Finance; Economics; Computer science; Psychology; Management","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002146294,0.0001504965,0.0003688629,0.0003459922,0.0009615109,0.00006760604,0.0002565862,0.00004329591,0.0000785376],"category_scores_gemma":[0.0005265204,0.0001404101,0.00008596834,0.0002985438,0.00008874793,0.0002123943,0.0002597652,0.000471794,0.000004345359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003115202,"about_ca_system_score_gemma":0.00008820423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008904695,"about_ca_topic_score_gemma":0.00003026598,"domain_scores_codex":[0.9986092,0.00005396592,0.000704832,0.0002306071,0.0001205597,0.0002808587],"domain_scores_gemma":[0.9988131,0.0001440194,0.0007185102,0.0001648165,0.00002288101,0.0001366934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001772505,0.00003149211,0.9515036,0.0001063427,0.00002661563,0.00003942779,0.008103263,0.002532859,0.000003122831,0.009140079,0.0008908047,0.02744513],"study_design_scores_gemma":[0.001525086,0.0001551221,0.7065122,0.00001139833,0.00002588386,0.0001490057,0.0005094532,0.0002076779,6.538434e-7,0.008545649,0.282191,0.0001668845],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897019,0.002230165,0.006493215,0.0005218526,0.0005015227,0.0002143406,0.00005787057,0.00001467105,0.0002644611],"genre_scores_gemma":[0.9930612,0.00453067,0.0001995954,0.001784879,0.000228404,0.00001507643,0.000001468771,0.00001387804,0.0001647758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2813002,"threshold_uncertainty_score":0.7395262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04542587833328747,"score_gpt":0.2462190660614427,"score_spread":0.2007931877281552,"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."}}