{"id":"W3014941101","doi":"10.1017/s0007087420000059","title":"Programming the USSR: Leonid V. Kantorovich in context","year":2020,"lang":"en","type":"article","venue":"The British Journal for the History of Science","topic":"Economic Theory and Institutions","field":"Economics, Econometrics and Finance","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Bundesministerium für Bildung und Forschung; Université du Québec à Montréal","keywords":"Ideology; Prosperity; Technocracy; Politics; Scholarship; Soviet union; Context (archaeology); State (computer science); Sociology; Political economy; Social science; Political science; Law; History; Archaeology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001715345,0.0003070263,0.0003486091,0.000610129,0.003312432,0.002942445,0.0004049101,0.00096218,0.004536192],"category_scores_gemma":[0.003173724,0.0002393452,0.0001867534,0.0009333084,0.007153988,0.003510222,0.002190931,0.004272567,0.0007630508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004373483,"about_ca_system_score_gemma":0.00266843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006136652,"about_ca_topic_score_gemma":0.00672341,"domain_scores_codex":[0.9986053,0.0009421781,0.00004900345,0.0001408522,0.0001674447,0.00009513738],"domain_scores_gemma":[0.9990238,0.0007246371,0.00004956569,0.00004742744,0.00007238134,0.00008224291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001179806,0.000008888641,0.0001034665,0.00003958376,0.000003595373,0.00003992673,0.001348474,0.0006714351,0.00004230233,0.9555262,0.03458461,0.007619594],"study_design_scores_gemma":[0.00001381709,0.00001144223,0.0002583388,0.0002177236,0.000004328717,0.00006066015,0.0007811915,0.0009798752,0.0001401031,0.5726338,0.4248865,0.00001213139],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03037767,0.09514922,0.04836354,0.3283617,0.007615081,0.00003968365,0.0002308252,0.0004398543,0.4894225],"genre_scores_gemma":[0.8369827,0.03916905,0.02278817,0.02106685,0.003049055,0.0001626066,0.0001323922,0.0004392431,0.07620986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9966876,"threshold_uncertainty_score":0.03173196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0608442339942151,"score_gpt":0.229295657996362,"score_spread":0.1684514240021469,"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."}}