{"id":"W4312101583","doi":"10.1002/jee.20497","title":"Developing transdisciplinarity in first‐year engineering","year":2022,"lang":"en","type":"article","venue":"Journal of Engineering Education","topic":"Interdisciplinary Research and Collaboration","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Mount Saint Vincent University","funders":"","keywords":"Transdisciplinarity; Metacognition; Curriculum; Engineering education; Systems thinking; Psychology; Pedagogy; Qualitative research; Engineering; Engineering ethics; Mathematics education; Knowledge management; Sociology; Computer science; Cognition","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002440314,0.00007795057,0.0001624434,0.0008908217,0.0001135073,0.00009993814,0.000440783,0.00002466275,0.000134195],"category_scores_gemma":[0.0007398602,0.00007201939,0.00006872656,0.001373558,0.000005544957,0.0005475557,0.00008950021,0.0003682399,0.000006482276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005195807,"about_ca_system_score_gemma":0.0006317624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002808846,"about_ca_topic_score_gemma":0.00001598312,"domain_scores_codex":[0.9981758,0.00005316574,0.0005883927,0.000124561,0.0008864341,0.0001716168],"domain_scores_gemma":[0.9990612,0.0002458766,0.0001620389,0.0001452465,0.0003126682,0.00007295946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001754818,0.0003288394,0.01038339,0.00006317333,0.00002615897,0.00003141745,0.007521053,0.9442627,0.002902201,0.02479747,0.005210605,0.004297496],"study_design_scores_gemma":[0.002976832,0.001827562,0.4390241,0.0006323943,0.00003611785,0.0008216744,0.04683721,0.2665895,0.003146691,0.03286522,0.2038991,0.001343642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9516792,0.0002286867,0.0407903,0.004780706,0.00218631,0.0001179979,0.0000030191,0.00001127762,0.0002024399],"genre_scores_gemma":[0.9923429,0.00000500295,0.007148694,0.00001328663,0.000234004,0.00001849826,0.000001689382,0.000009557997,0.0002263143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6776733,"threshold_uncertainty_score":0.2936865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05166256657994814,"score_gpt":0.3796034474061781,"score_spread":0.32794088082623,"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."}}