{"id":"W3125676971","doi":"10.1177/23780231211006977","title":"The Pandemic Penalty: The Gendered Effects of COVID-19 on Scientific Productivity","year":2021,"lang":"en","type":"article","venue":"Socius Sociological Research for a Dynamic World","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Radcliffe Institute for Advanced Study, Harvard University; Santa Clara University","keywords":"Productivity; Pandemic; Prestige; Promotion (chess); Coronavirus disease 2019 (COVID-19); Political science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Sociology; Demographic economics; Public relations; Economics; Economic growth; Law; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch","bibliometrics","sts","scholarly_communication"],"consensus_categories":["metaresearch","sts"],"category_scores_codex":[0.1284975,0.000209786,0.0004784075,0.006478312,0.005145112,0.002168303,0.004069441,0.0002042847,0.000233229],"category_scores_gemma":[0.4739262,0.00009230318,0.0006980695,0.08641094,0.006363535,0.0001553467,0.001732753,0.001599382,0.0001115269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000836416,"about_ca_system_score_gemma":0.002628132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000306156,"about_ca_topic_score_gemma":0.0004101528,"domain_scores_codex":[0.9789476,0.005442449,0.000765358,0.001546284,0.01157837,0.001719903],"domain_scores_gemma":[0.8049811,0.185715,0.0002951861,0.001975447,0.006339148,0.0006941853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001046545,0.002384328,0.04807182,0.0004061475,0.0004694092,0.0001225336,0.003679321,0.0003232018,0.02100591,0.1990061,0.4293766,0.2941081],"study_design_scores_gemma":[0.0009657316,0.000635358,0.04245294,0.00001884795,0.00001351012,0.000006394007,0.003851979,0.005350644,0.000752579,0.745602,0.2001084,0.0002415409],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.927615,0.008321771,0.003512188,0.05175266,0.0015867,0.003447239,0.0001940302,0.00009374817,0.003476664],"genre_scores_gemma":[0.9677847,0.0005237709,0.0001643834,0.0002263243,0.000117767,0.0002859884,0.0000169018,0.00001442001,0.03086577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5465959,"threshold_uncertainty_score":0.9988676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.77604919358679,"score_gpt":0.6606782569336781,"score_spread":0.1153709366531119,"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."}}