{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01477325,0.0003638276,0.0005421389,0.002700761,0.01944597,0.01430809,0.001763246,0.002552317,0.003194956],"category_scores_gemma":[0.01235512,0.0001902219,0.0003271645,0.002186131,0.03317898,0.00603074,0.02709957,0.002599625,0.0001641476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01901636,"about_ca_system_score_gemma":0.04045293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1255174,"about_ca_topic_score_gemma":0.1612548,"domain_scores_codex":[0.9854042,0.006540535,0.0002949497,0.0007641208,0.003328103,0.003668019],"domain_scores_gemma":[0.9937948,0.002439559,0.0004501259,0.0003139612,0.001009666,0.001991832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00004698157,0.0001050026,0.01150713,0.0001599269,0.00002505105,0.0003196,0.09090456,0.0008418108,0.00052851,0.7879303,0.003144166,0.1044869],"study_design_scores_gemma":[0.00003687193,0.0001522895,0.02225949,0.0009203225,0.0000353328,0.0003417664,0.2100517,0.0009465652,0.001588264,0.5537815,0.2098184,0.0000676521],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.423934,0.00779448,0.0178708,0.06847507,0.0003509771,0.0001495569,0.0000435223,0.00005417079,0.4813274],"genre_scores_gemma":[0.988717,0.000620445,0.001117819,0.001526584,0.0000327143,0.00002819329,0.000008468311,0.00001051857,0.007938144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1255174,"threshold_uncertainty_score":0.2495735,"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."}}