{"id":"W2766589242","doi":"10.1353/kri.2017.0054","title":"\"All in Good Conscience\": In Memory of Michelle Lamarche Marrese (1964–2016)","year":2017,"lang":"en","type":"article","venue":"Kritika","topic":"Soviet and Russian History","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scholarship; Conscience; Narrative; History; State (computer science); Sociology; Classics; Art history; Law; Literature; Art; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001360481,0.00007371404,0.0001811403,0.0001295564,0.000276271,0.00004046935,0.0006721123,0.0001178114,0.000315287],"category_scores_gemma":[0.0005545889,0.00007305016,0.00005053766,0.0001298652,0.001160185,0.0001780711,0.00008947621,0.0001696818,0.00006766075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001215198,"about_ca_system_score_gemma":0.0003951248,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01564514,"about_ca_topic_score_gemma":0.03557966,"domain_scores_codex":[0.9988067,0.0001613932,0.0002058246,0.0002065185,0.0002849552,0.0003346522],"domain_scores_gemma":[0.9993141,0.0001233013,0.0001089012,0.0003332853,0.00002817423,0.00009227858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001056119,0.0008600637,0.6760361,0.0001616663,0.00002537169,0.0003287029,0.1230048,0.000007459789,0.008564539,0.1167495,0.03960781,0.03454833],"study_design_scores_gemma":[0.001571284,0.00004322506,0.7662091,0.0002216426,0.000008041528,0.000001268556,0.01045023,0.00003074978,0.0006901355,0.008163908,0.212264,0.0003464005],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4298917,0.0005244717,5.172521e-7,0.002496472,0.0004313474,0.0001443882,0.000004053578,0.00001040717,0.5664967],"genre_scores_gemma":[0.9908664,0.0001952822,0.0001613296,0.0001325826,0.0001051337,0.00001143309,6.764313e-7,0.000006120108,0.008521071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5609747,"threshold_uncertainty_score":0.9909098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06348814132209654,"score_gpt":0.3562774636909314,"score_spread":0.2927893223688349,"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."}}