{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01203788,0.0003450097,0.0004859384,0.00293258,0.01635687,0.01447662,0.00145839,0.002120098,0.004426335],"category_scores_gemma":[0.0149192,0.0001834556,0.0002620454,0.002346859,0.03068202,0.005342698,0.02362432,0.002385261,0.0002307099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01248433,"about_ca_system_score_gemma":0.01923635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05914494,"about_ca_topic_score_gemma":0.06812966,"domain_scores_codex":[0.9855942,0.007037156,0.0002937525,0.0008599731,0.003091278,0.003123577],"domain_scores_gemma":[0.9920603,0.003172702,0.0007430768,0.0004230032,0.001110013,0.002490945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006643453,0.0001632664,0.02610744,0.0001408976,0.000025765,0.0003900506,0.1790838,0.0005207533,0.0005593012,0.6704006,0.003037768,0.1195038],"study_design_scores_gemma":[0.0000434506,0.0001651594,0.0399016,0.0009642048,0.00002938773,0.0004924245,0.3565738,0.0008805762,0.001144969,0.4029892,0.1967509,0.0000643294],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.5750877,0.004482725,0.009722126,0.03449559,0.0002271176,0.00008904786,0.00003472804,0.00002908427,0.3758318],"genre_scores_gemma":[0.9921839,0.0004012565,0.000525721,0.0008456937,0.00002874785,0.00001898509,0.000007324053,0.000008163687,0.00598025],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.05914494,"threshold_uncertainty_score":0.1176013,"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."}}