{"id":"W4250503452","doi":"10.32920/14668977","title":"Gender-Heterogeneous Working Groups Produce Higher Quality Science","year":2021,"lang":"en","type":"preprint","venue":"","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Rice University; National Science Foundation","keywords":"Gender diversity; Diversity (politics); Quality (philosophy); Psychology; Representation (politics); Gender disparity; Principal (computer security); Political science; Sociology; Gender studies; Management; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["metaresearch","bibliometrics","open_science"],"category_scores_codex":[0.0813081,0.000492797,0.0009589929,0.07304206,0.0008027331,0.02544792,0.01219101,0.0004603262,0.003577359],"category_scores_gemma":[0.05030537,0.0003503293,0.0005490514,0.2743039,0.001012586,0.0006848926,0.02112222,0.001461315,0.0003721919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006275946,"about_ca_system_score_gemma":0.002867861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003222448,"about_ca_topic_score_gemma":0.00004318705,"domain_scores_codex":[0.9572161,0.0007933922,0.001903367,0.004683132,0.03371556,0.00168844],"domain_scores_gemma":[0.9790397,0.003577047,0.0008657982,0.005154808,0.01020878,0.001153887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00006707724,0.001454691,0.3524103,0.0001808979,0.00019398,0.0004605066,0.001249398,0.003131649,0.01385782,0.01476742,0.01793139,0.5942948],"study_design_scores_gemma":[0.0007226964,0.0001222511,0.8504038,0.0001115204,0.00003688151,0.00006280331,0.001291219,0.008952701,0.02785382,0.06984645,0.03796791,0.00262796],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8887272,0.003675605,0.008247669,0.001187169,0.007568753,0.0006846273,0.00002994295,0.0001429398,0.08973605],"genre_scores_gemma":[0.9857101,0.0002346875,0.006048811,0.0003845043,0.0004722772,0.00003943018,0.0000119151,0.0000289179,0.007069311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5916669,"threshold_uncertainty_score":0.9998949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8309185971340006,"score_gpt":0.6203364727099676,"score_spread":0.210582124424033,"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."}}