{"id":"W4205432505","doi":"10.32920/ryerson.17847683.v1","title":"Integrating Diversity of Users' Human Factors Into A Cornerstone Engineering Design Course","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Design Education and Practice","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cornerstone; Deliverable; Diversity (politics); Inclusion (mineral); Engineering design process; Process (computing); Computer science; Phase (matter); Engineering management; Engineering; Psychology; Systems engineering; Mechanical engineering; Sociology","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004761089,0.0003054985,0.0003557967,0.0002072804,0.0001770532,0.00004582835,0.0004771342,0.0001663838,0.002493345],"category_scores_gemma":[0.0001238223,0.0003366636,0.0001272733,0.0001885489,0.00002623727,0.0001500708,0.0007540326,0.001012415,0.000008201878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002985452,"about_ca_system_score_gemma":0.00008801063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001040737,"about_ca_topic_score_gemma":0.00003297745,"domain_scores_codex":[0.998824,0.00008752621,0.0003430977,0.0002610629,0.0002786818,0.0002056201],"domain_scores_gemma":[0.9989231,0.0003466999,0.0001379695,0.0004186026,0.00006629303,0.0001072824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000193749,0.0002164805,0.009387522,0.001001662,0.000734542,0.00001234799,0.03053088,0.9323852,0.009102685,0.003556496,0.01245544,0.0005973512],"study_design_scores_gemma":[0.00286501,0.0007804342,0.06910068,0.001284042,0.00276081,0.00003046725,0.106056,0.7030287,0.06771705,0.004609,0.03270896,0.009058857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5508054,0.0004405593,0.4276286,0.00007420593,0.003949536,0.000835892,0.0000375145,0.001297369,0.01493096],"genre_scores_gemma":[0.9831317,0.00003007316,0.01609438,0.00001647506,0.00004951515,0.00002557397,0.00007034975,0.00005298962,0.0005289263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4323263,"threshold_uncertainty_score":0.9999086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05082031857487797,"score_gpt":0.2882139620551842,"score_spread":0.2373936434803062,"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."}}