{"id":"W4378472018","doi":"10.15695/jstem/v5i2.12","title":"Approaches for Measuring Inclusive Demographics Across Youth Enjoy Science Cancer Research Training Programs","year":2023,"lang":"en","type":"article","venue":"The Journal of STEM Outreach","topic":"Diversity and Career in Medicine","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Cancer Research","funders":"Comprehensive Cancer Center, University of Chicago Medical Center; University of Chicago Medicine; National Institute of General Medical Sciences; National Institute of Mental Health; National Institutes of Health; Directorate for Biological Sciences; National Cancer Institute; Georgia Clinical and Translational Science Alliance; Dana-Farber/Harvard Cancer Center; Dartmouth College; National Center for Advancing Translational Sciences; Oregon Health and Science University","keywords":"Demographics; Operationalization; Workforce; Medical education; Diversity (politics); Descriptive statistics; Thematic analysis; Survey data collection; Psychology; Medicine; Political science; Sociology; Demography; Social science; Qualitative research","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05269501,0.0007660354,0.0006293079,0.01076647,0.003853207,0.003436485,0.002173735,0.0007273892,0.002955971],"category_scores_gemma":[0.09061506,0.0005834812,0.001048628,0.006948565,0.00177775,0.005132777,0.008125743,0.00141569,0.0005096258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003696939,"about_ca_system_score_gemma":0.008421089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008696343,"about_ca_topic_score_gemma":0.01995555,"domain_scores_codex":[0.9692791,0.01736397,0.005091793,0.001759351,0.005229301,0.001276569],"domain_scores_gemma":[0.9250342,0.02577822,0.02047932,0.007021615,0.01849223,0.003194422],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000154636,0.0007619299,0.7578316,0.0009173238,0.0002058026,0.000110421,0.04918852,0.0006960394,0.001749224,0.008070156,0.004588273,0.175726],"study_design_scores_gemma":[0.00005270827,0.0008106265,0.8728583,0.0008574796,0.0001546366,0.000175622,0.0893165,0.002819778,0.002887032,0.007111388,0.02283676,0.0001190342],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8126081,0.0007539354,0.1289005,0.002482676,0.000229728,0.01656027,0.004562395,0.000406051,0.03349647],"genre_scores_gemma":[0.7675717,0.0006509033,0.1938527,0.0007752557,0.00007487783,0.03160195,0.003589272,0.00008149986,0.001801847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.947305,"threshold_uncertainty_score":0.2786812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5980393011256838,"score_gpt":0.4604366418757976,"score_spread":0.1376026592498862,"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."}}