{"id":"W4389249335","doi":"10.1109/ms.2023.3306132","title":"Thinking Fast and Slow in Software Engineering","year":2023,"lang":"en","type":"article","venue":"IEEE Software","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Software engineering; Social software engineering; Semantics (computer science); Software development; Software; Software construction; Software system; Programming language; Data science","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":[0.001056874,0.0001003869,0.0001467707,0.0001366184,0.00071356,0.0001779542,0.0002080457,0.0001960305,0.00001675066],"category_scores_gemma":[0.002960011,0.0001102697,0.00004118159,0.0005574301,0.0001104124,0.0003345194,0.00007829433,0.000315509,0.00003432484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007224913,"about_ca_system_score_gemma":0.0001230103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001544026,"about_ca_topic_score_gemma":0.002536649,"domain_scores_codex":[0.9988602,0.00005320976,0.0001337383,0.0001879995,0.0003354173,0.0004293983],"domain_scores_gemma":[0.999106,0.0005482283,0.00003334567,0.00009946487,0.00007778367,0.000135121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00001536446,0.00006877237,0.3498929,0.0002488871,0.00006352853,0.0002958915,0.5437252,0.002597959,0.0002272935,0.03282791,0.01078751,0.05924886],"study_design_scores_gemma":[0.002719905,0.0002438302,0.5290931,0.001602951,0.00007017459,0.000007915535,0.04967782,0.001350384,0.0004186536,0.2702707,0.1413257,0.003218762],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906135,0.0002220906,0.001360063,0.004348028,0.001141701,0.0002332613,0.0000175082,0.00110287,0.0009609854],"genre_scores_gemma":[0.994737,0.0003740111,0.002799301,0.0006117853,0.000373252,0.0000120591,0.000005339628,0.00003069413,0.0010565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4940473,"threshold_uncertainty_score":0.5488199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02947401924925046,"score_gpt":0.3143461962366311,"score_spread":0.2848721769873807,"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."}}