{"id":"W4403258249","doi":"10.1177/21582440241286217","title":"Research Output, Key Topics, and Trends in Productivity, Visibility, and Collaboration in Social Sciences Research on COVID-19: A Scientometric Analysis and Visualization","year":2024,"lang":"en","type":"article","venue":"SAGE Open","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Visibility; Productivity; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Visualization; Data science; Key (lock); Regional science; Sociology; Computer science; Geography; Economics; Economic growth; Biology; Medicine; Virology; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","scholarly_communication"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.1826105,0.0001404556,0.0004050074,0.316676,0.00122006,0.01939165,0.001524382,0.0001456938,0.0001430942],"category_scores_gemma":[0.0484876,0.0001070535,0.00003559464,0.7127522,0.001470709,0.001858031,0.00324252,0.0005616667,0.00001217183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005416667,"about_ca_system_score_gemma":0.0008008491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002820311,"about_ca_topic_score_gemma":0.009032195,"domain_scores_codex":[0.9845275,0.002928143,0.0006227181,0.00208999,0.009039807,0.0007918592],"domain_scores_gemma":[0.9911966,0.006513589,0.00007541089,0.0004475643,0.001374339,0.0003924498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008199544,0.0002535636,0.5628499,0.00004165246,0.00001880837,0.00003907341,0.003633512,0.00005647703,0.0001099798,0.01338576,0.003567338,0.415962],"study_design_scores_gemma":[0.0005794233,0.0005209752,0.9323789,0.00002804311,0.000008163898,0.0000024253,0.004283262,0.02240468,0.00009778956,0.02895606,0.01055211,0.0001881388],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771258,0.002026113,0.0002574512,0.01316461,0.0001180664,0.0008376808,0.00004044853,0.0000133651,0.006416447],"genre_scores_gemma":[0.995627,0.0003722777,0.0001600191,0.00006102857,0.00007406418,0.0000474541,0.000007096333,0.000007610881,0.003643421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4157738,"threshold_uncertainty_score":0.9816263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.791546586054148,"score_gpt":0.7291634070649856,"score_spread":0.06238317898916235,"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."}}