{"id":"W4292651799","doi":"10.5195/names.2022.2438","title":"Using Onomastics to Inform Diversity Initiatives","year":2022,"lang":"en","type":"article","venue":"Names","topic":"Names, Identity, and Discrimination Research","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of Pittsburgh","keywords":"Onomastics; Demographics; Diversity (politics); Census; Ethnic group; Race (biology); Indigenous; Workforce; Demography; Medicine; Geography; Political science; Gender studies; Population; Sociology; Anthropology; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004857213,0.00003799739,0.00006068069,0.0001471841,0.003478132,0.00008121326,0.0002576138,0.00001770561,0.001673767],"category_scores_gemma":[0.0004805965,0.00004423907,0.00003195021,0.0004488085,0.0001076365,0.0002656348,0.0007471099,0.0000984654,0.00002881454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000269583,"about_ca_system_score_gemma":0.0002118676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002710184,"about_ca_topic_score_gemma":0.0009513719,"domain_scores_codex":[0.9989104,0.000106029,0.00007212989,0.00009350121,0.0006286413,0.0001893007],"domain_scores_gemma":[0.999618,0.00008252906,0.0000296408,0.00006465914,0.0001037381,0.0001014332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003372558,0.0001591589,0.6008723,0.00001903942,0.00002476006,0.00001723886,0.1021809,0.0004778159,0.00003295498,0.2788992,0.0091669,0.008116044],"study_design_scores_gemma":[0.0007267381,0.0002240179,0.2905487,0.00001058841,0.00004339689,0.000001447755,0.2774777,0.0005671158,0.0001205789,0.01720538,0.4125522,0.0005220881],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7865936,0.00007401959,0.0005985061,0.00198945,0.0004349434,0.0002325563,0.00003449662,0.00005534995,0.2099871],"genre_scores_gemma":[0.9952576,0.00001086198,0.0003770124,0.0003094996,0.00007309359,0.000005955806,0.000002714354,0.000002746435,0.003960548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4033853,"threshold_uncertainty_score":0.9992388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1885891702060451,"score_gpt":0.4310567464026231,"score_spread":0.242467576196578,"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."}}