{"id":"W2954648659","doi":"10.1007/978-3-030-20470-9_8","title":"Leveraging Disciplinary and Cultural Diversity in the Conceptualization Stages of Design","year":2019,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Design Education and Practice","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Conceptualization; Viewpoints; Discipline; Leverage (statistics); Embodied cognition; Scope (computer science); Cross disciplinary; Engineering ethics; Diversity (politics); Cultural diversity; Knowledge management; Sociology; Computer science; Data science; Engineering; Social science; Artificial intelligence; Anthropology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.01173819,0.0007661223,0.0004729973,0.001872757,0.002701933,0.01266512,0.001702821,0.001483317,0.003516332],"category_scores_gemma":[0.0140127,0.0007636586,0.000735921,0.00134247,0.0237699,0.01205789,0.005584451,0.005035057,0.0005721764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004658423,"about_ca_system_score_gemma":0.005604705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003813483,"about_ca_topic_score_gemma":0.006854592,"domain_scores_codex":[0.9916493,0.006148749,0.0003214662,0.0004890704,0.001108278,0.0002832272],"domain_scores_gemma":[0.9866628,0.009486836,0.000458953,0.001986522,0.0009818795,0.0004230507],"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":[0.00001441584,0.00002574445,0.0005863195,0.0001169957,0.00001199308,0.00008435985,0.03594453,0.001054751,0.0007558548,0.9206635,0.0008755577,0.03986589],"study_design_scores_gemma":[0.00001189112,0.0000477954,0.001086414,0.0006800836,0.00002570247,0.0003601284,0.01378834,0.004408257,0.001590207,0.8749487,0.1030063,0.00004627782],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04880559,0.007614505,0.4894193,0.007686791,0.0002602282,0.0002059675,0.00004787845,0.0001884769,0.4457712],"genre_scores_gemma":[0.7156418,0.004721592,0.2459942,0.0008782415,0.00008163396,0.0002372483,0.0001156693,0.0003504241,0.03197916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01266512,"threshold_uncertainty_score":0.06207818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06779556311143396,"score_gpt":0.3043715942817481,"score_spread":0.2365760311703141,"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."}}