{"id":"W3171006794","doi":"10.3917/riges.462.0090","title":"On a lu pour vous – Faire la morale aux robots, de Martin Gibert","year":2021,"lang":"fr","type":"article","venue":"Gestion","topic":"Health, Medicine and Society","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy; Programmer; Computer science; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.003359172,0.0009938745,0.000567027,0.0009437195,0.004221569,0.007314977,0.000933558,0.004822029,0.01967234],"category_scores_gemma":[0.0142778,0.0004388083,0.000540851,0.0005888408,0.0107864,0.009857947,0.002605471,0.009761127,0.008929659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002851757,"about_ca_system_score_gemma":0.0020639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005771191,"about_ca_topic_score_gemma":0.005872013,"domain_scores_codex":[0.9971223,0.001303367,0.00007777794,0.0006460683,0.0006815958,0.0001688538],"domain_scores_gemma":[0.9954479,0.002607546,0.0002819794,0.0003475907,0.0007475591,0.000567383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009321079,0.00002585851,0.0004309818,0.0001911239,0.00002113783,0.0001090897,0.002717545,0.0003576285,0.0005808998,0.5207766,0.4444606,0.03023525],"study_design_scores_gemma":[0.00001131838,0.00002220385,0.0002087489,0.0002183995,0.000005980206,0.0001858882,0.00102335,0.0003134718,0.000390882,0.1007211,0.8968656,0.00003314844],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.002919089,0.07182579,0.02336757,0.7478108,0.02974078,0.00003750508,0.000204549,0.0003920537,0.1237018],"genre_scores_gemma":[0.1440504,0.06065305,0.02262047,0.224318,0.01874932,0.000174099,0.000258228,0.001160431,0.5280159],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.01967234,"threshold_uncertainty_score":0.0658105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04947704934233157,"score_gpt":0.38874495376353,"score_spread":0.3392679044211985,"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."}}