{"id":"W3107651095","doi":"10.5539/hes.v10n4p131","title":"Mining for Untapped Talent and Overcoming Challenges to Diversity in Higher Education: Evidence for Inclusive Academic Programs","year":2020,"lang":"en","type":"article","venue":"Higher Education Studies","topic":"Higher Education Research Studies","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Graduation (instrument); Diversity (politics); Higher education; Underrepresented Minority; Inclusion (mineral); Population; Political science; Historically black colleges and universities; Academic achievement; Enrollment management; Medical education; Psychology; Public relations; Sociology; Pedagogy; Economic growth; Social science; Engineering; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0201221,0.0003387591,0.0006547456,0.002049234,0.004588995,0.00879872,0.002888185,0.00152474,0.008203895],"category_scores_gemma":[0.07925425,0.000270612,0.0006483204,0.0030947,0.005142677,0.006278128,0.01364662,0.00313144,0.0005440805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002154637,"about_ca_system_score_gemma":0.01549949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005142833,"about_ca_topic_score_gemma":0.01867661,"domain_scores_codex":[0.9854988,0.009137549,0.000466488,0.0007176208,0.002763832,0.001415657],"domain_scores_gemma":[0.9144043,0.05697044,0.01126592,0.003645967,0.005313213,0.0084001],"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":[0.0005736664,0.00321208,0.1476795,0.005078101,0.0005599838,0.0002672954,0.02355037,0.0003035054,0.0002286344,0.02998229,0.008355387,0.7802092],"study_design_scores_gemma":[0.0002902774,0.002733214,0.5826783,0.03640994,0.0018549,0.0005121085,0.1730416,0.000835916,0.001526272,0.06531023,0.1346788,0.000128556],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7398422,0.0473343,0.00255275,0.1079801,0.0007623368,0.0004680601,0.0002934519,0.00005416549,0.1007126],"genre_scores_gemma":[0.9714288,0.01913201,0.001664275,0.005334714,0.000203171,0.0001945339,0.00009976969,0.00002301662,0.001919793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0201221,"threshold_uncertainty_score":0.1064171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4471548329271176,"score_gpt":0.5245295223940029,"score_spread":0.0773746894668853,"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."}}