{"id":"W1953616297","doi":"10.24908/pceea.v0i0.5758","title":"ENCOURAGING EMPATHY IN ENGINEERING DESIGN","year":2015,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Design Education and Practice","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Empathy; Engineering design process; Creativity; Generative grammar; Experiential learning; Perception; Merge (version control); Computer science; Knowledge management; Engineering; Psychology; Engineering ethics; Social psychology; Mathematics education; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001049269,0.0001835668,0.0001780859,0.0005360702,0.00005102178,0.0001225263,0.0003154445,0.0001527875,0.00002014716],"category_scores_gemma":[0.001860546,0.0002022945,0.00005342629,0.0009255505,0.000006824578,0.0004913232,0.00001427146,0.0003561981,0.00002652196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003514783,"about_ca_system_score_gemma":0.0008324892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002367096,"about_ca_topic_score_gemma":0.001128277,"domain_scores_codex":[0.9987869,0.00001061349,0.0003372697,0.0001563641,0.0003257361,0.0003830918],"domain_scores_gemma":[0.9989384,0.0001057859,0.0001429375,0.0001126598,0.0003782283,0.0003220298],"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":[0.00001175501,0.000172707,0.06602462,0.0007445631,0.0002074407,9.587321e-7,0.02102002,0.6627707,0.008246325,0.01006441,0.2273501,0.003386294],"study_design_scores_gemma":[0.001662498,0.0000802449,0.1724167,0.00108484,0.0001877778,0.00005393715,0.006992738,0.4949195,0.02092481,0.001099845,0.298237,0.002340151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8801912,0.002407446,0.005080688,0.01528702,0.02730336,0.003100737,0.00004377783,0.001719995,0.06486574],"genre_scores_gemma":[0.9952511,0.0000145655,0.00353373,0.000135189,0.0001900858,0.00007735124,0.000003598348,0.00005571226,0.0007387248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1678512,"threshold_uncertainty_score":0.9191036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01536800325564701,"score_gpt":0.2106698085192265,"score_spread":0.1953018052635795,"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."}}