{"id":"W3135175820","doi":"10.1007/s11192-021-03911-4","title":"Digital technology helps remove gender bias in academia","year":2021,"lang":"en","type":"article","venue":"Scientometrics","topic":"Diversity and Career in Medicine","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Meritocracy; Gender bias; Altmetrics; Metric (unit); Gender disparity; Productivity; Psychology; Sociology; Political science; Computer science; Library science; Social psychology; Gender studies; Marketing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["metaresearch"],"domain":"incentives","study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["metaresearch"],"domain":"evaluation","study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.02179132,0.0004103195,0.0007185418,0.005411915,0.002367332,0.004612849,0.001014684,0.001233421,0.01506916],"category_scores_gemma":[0.1306876,0.0002419981,0.0007626347,0.008399528,0.001370776,0.004402961,0.003783525,0.0008514216,0.002533885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001292102,"about_ca_system_score_gemma":0.004689829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003250388,"about_ca_topic_score_gemma":0.005625691,"domain_scores_codex":[0.9847047,0.006487811,0.00136686,0.001697633,0.004730598,0.001012283],"domain_scores_gemma":[0.8800156,0.0790465,0.01348466,0.01197529,0.01188721,0.003590714],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006635761,0.0005707162,0.3761347,0.001020025,0.0003900416,0.0003384043,0.007830255,0.001218707,0.004355365,0.0291173,0.02721128,0.5511496],"study_design_scores_gemma":[0.0002809401,0.0007221096,0.7200807,0.001267259,0.0008790285,0.0005089535,0.01625875,0.004428311,0.01396675,0.08135785,0.1601139,0.0001353786],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8360416,0.003886332,0.03135788,0.02519791,0.002671771,0.0002420982,0.002981049,0.0005587508,0.09706263],"genre_scores_gemma":[0.9780973,0.0006999173,0.009710665,0.002130833,0.001095866,0.000115815,0.0005520565,0.0001416672,0.007455844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9945881,"threshold_uncertainty_score":0.1152449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1477633997988055,"score_gpt":0.3722449533529507,"score_spread":0.2244815535541453,"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."}}