{"id":"W4381885715","doi":"10.1109/ethics57328.2023.10155041","title":"ETHICS-2023 Session A2 - Panel: The arc of a global engineering education","year":2023,"lang":"en","type":"article","venue":"","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Engineering education; Government (linguistics); Session (web analytics); Engineering ethics; Engineering management; Engineering; Political science; Resilience (materials science); Medical education; Business; Medicine","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":[0.000230428,0.00009258384,0.00008424617,0.00005011342,0.00003076125,0.00001313637,0.0001364941,0.0001141417,0.00005376869],"category_scores_gemma":[0.0001064443,0.00006584712,0.00003732053,0.0007791572,0.0000206105,0.00005232396,0.00002611701,0.0002101342,0.0001086934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005844187,"about_ca_system_score_gemma":0.00006927564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006415589,"about_ca_topic_score_gemma":0.000006514811,"domain_scores_codex":[0.9993919,0.00000916982,0.0001579486,0.00008793222,0.000182068,0.0001710292],"domain_scores_gemma":[0.9996187,0.0001009035,0.00001238632,0.0001728484,0.00003203753,0.00006311473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001619422,0.0002988995,0.004434267,0.008967941,0.0003203951,0.000004985246,0.005641356,0.2692217,0.09475093,0.07344686,0.3674903,0.1754063],"study_design_scores_gemma":[0.0003925845,0.00006207159,0.2941615,0.0008328925,0.0000632071,0.00002893,0.002242729,0.58878,0.009093778,0.003294469,0.1002579,0.0007899303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9452374,0.0007221196,0.02517838,0.007774049,0.008377191,0.0003476797,0.00002569755,0.002860845,0.009476656],"genre_scores_gemma":[0.9987246,0.00009646922,0.0005392909,0.00004848747,0.0001982876,0.00003034318,0.00002449588,0.00001605424,0.0003219317],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3195583,"threshold_uncertainty_score":0.2685167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02526119692145488,"score_gpt":0.2723255942153068,"score_spread":0.2470643972938519,"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."}}