{"id":"W4391110585","doi":"10.3390/educsci14010110","title":"Equity, Diversity, and Inclusion Strategies in Engineering and Computer Science","year":2024,"lang":"en","type":"article","venue":"Education Sciences","topic":"Disability Education and Employment","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Equity (law); Diversity (politics); Inclusion (mineral); Government (linguistics); Political science; Public relations; Higher education; Science and engineering; Underrepresented Minority; Set (abstract data type); Engineering ethics; Knowledge management; Engineering management; Sociology; Computer science; 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"],"consensus_categories":[],"category_scores_codex":[0.001917471,0.00004440981,0.00004412307,0.0002495357,0.003203839,0.0006372521,0.0002467931,0.00001794709,0.00003544757],"category_scores_gemma":[0.00008559431,0.00004085249,0.000006697666,0.0008745063,0.001176034,0.001114167,0.003406333,0.00004484317,0.0000033462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001212602,"about_ca_system_score_gemma":0.001232994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002360088,"about_ca_topic_score_gemma":0.0006053089,"domain_scores_codex":[0.9990419,0.00002622323,0.00007958683,0.0002498852,0.000433092,0.0001693149],"domain_scores_gemma":[0.9997154,0.00009308534,0.00001154556,0.00004686897,0.00003580337,0.000097223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[4.278514e-7,0.0000668993,0.04084436,0.00004578224,8.921454e-7,2.349835e-7,0.3024902,0.00004943912,0.0001027418,0.5475411,0.0001730422,0.1086848],"study_design_scores_gemma":[0.00019216,0.0001655172,0.4325933,0.0008752128,0.00001546357,0.000007575167,0.2259927,0.02118883,0.0001450202,0.2697087,0.04838927,0.0007262151],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812813,0.0005828455,0.0001708036,0.007334935,0.001278799,0.0001024588,2.854424e-7,0.00004178623,0.009206807],"genre_scores_gemma":[0.9989517,0.0001012975,0.0006185472,0.0001201888,0.00009645261,0.000004243032,2.006795e-7,0.000001157836,0.000106237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.391749,"threshold_uncertainty_score":0.9980938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06721878443500857,"score_gpt":0.4082657724309539,"score_spread":0.3410469879959453,"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."}}