{"id":"W4389752816","doi":"10.20944/preprints202312.0860.v1","title":"Equity, Diversity, and Inclusion Strategies in Engineering and Computer Science","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Disability Education and Employment","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Equity (law); Diversity (politics); Inclusion (mineral); Government (linguistics); Political science; Underrepresented Minority; Public relations; Science and engineering; Gender equity; Engineering ethics; Sociology; Engineering; Medical education; Social science","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","open_science"],"consensus_categories":[],"category_scores_codex":[0.003249907,0.0001322986,0.0001757478,0.0002179811,0.002228809,0.0001252535,0.0006538902,0.0001219569,0.00006241717],"category_scores_gemma":[0.0002705564,0.0001510308,0.00002685619,0.0002630964,0.0006268026,0.000273031,0.1317596,0.0002995002,0.00003340907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003527852,"about_ca_system_score_gemma":0.000407667,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01376523,"about_ca_topic_score_gemma":0.002959824,"domain_scores_codex":[0.9981862,0.00006964025,0.0001881533,0.0006292251,0.0005789388,0.0003477856],"domain_scores_gemma":[0.9992998,0.00009104187,0.00006030384,0.0002934232,0.00007901496,0.0001764143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002951451,0.00005746072,0.8277409,0.0001350802,0.000005965002,0.000001799103,0.1603902,0.0007425395,0.0001094497,0.009739983,0.000006223054,0.001067495],"study_design_scores_gemma":[0.0001080949,0.000006097997,0.9653952,0.000154894,0.000007542903,3.000956e-7,0.004963782,0.001017678,0.00007236153,0.02760478,0.0004589774,0.0002103538],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935312,0.00002895634,0.00009567863,0.002288185,0.0007833023,0.0003582268,0.000002449456,0.0001306331,0.002781411],"genre_scores_gemma":[0.9993115,0.0002247815,0.0001314907,0.00005834789,0.0001024828,0.00001738659,0.0000021863,0.000007317528,0.000144521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1554264,"threshold_uncertainty_score":0.9990702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2195821647069495,"score_gpt":0.4207550097682423,"score_spread":0.2011728450612928,"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."}}