{"id":"W4396858434","doi":"10.18260/1-2--45707","title":"Building Interdisciplinarity in Engineering Doctoral Education: Insights from DTAIS Summer Incubator","year":2024,"lang":"en","type":"article","venue":"","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Goddard Space Flight Center; European Space Agency; University of Toronto; Massachusetts Institute of Technology; National Aeronautics and Space Administration","keywords":"Incubator; Scholarship; Engineering ethics; Cornerstone; Context (archaeology); Bridge (graph theory); Transdisciplinarity; Discipline; Computer science; Engineering management; Engineering; Sociology; Knowledge management; Political science; Social science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0208375,0.0003076328,0.0004930832,0.001470556,0.01780383,0.01670247,0.001794269,0.003377112,0.007419985],"category_scores_gemma":[0.01744745,0.0003741448,0.000404694,0.002136792,0.01420465,0.009172997,0.02205107,0.005463318,0.001767011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01010497,"about_ca_system_score_gemma":0.02286178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00448922,"about_ca_topic_score_gemma":0.01254672,"domain_scores_codex":[0.9816023,0.01356642,0.0002557183,0.0007120881,0.001388875,0.002474703],"domain_scores_gemma":[0.9664938,0.009337129,0.001351246,0.001221169,0.002055992,0.01954059],"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":[0.000200458,0.0006989852,0.01167323,0.0002269591,0.00001851233,0.003002713,0.3810681,0.000924381,0.001002093,0.5286312,0.03576637,0.03678707],"study_design_scores_gemma":[0.00004396416,0.0001601317,0.006315854,0.0003312684,0.000009191138,0.0006272656,0.4924712,0.001326688,0.0004974671,0.1049039,0.3932426,0.00007054093],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4936245,0.003068412,0.0153122,0.1204911,0.001752022,0.0002473971,0.0001560263,0.0002244143,0.365124],"genre_scores_gemma":[0.966006,0.001518114,0.003108962,0.004538137,0.0002049425,0.0001568356,0.00007611986,0.0001056056,0.02428519],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0208375,"threshold_uncertainty_score":0.1102005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04489134609293095,"score_gpt":0.3996657991847407,"score_spread":0.3547744530918098,"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."}}