{"id":"W3123793396","doi":"","title":"Social Network Analysis: A Complementary Method of Discovery for the History of Economics","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Historical Economic and Social Studies","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université de Sherbrooke","funders":"","keywords":"Toolbox; Data science; Social network analysis; Computer science; Social network (sociolinguistics); Representation (politics); Quantitative history; Management science; Social science; Sociology; Economics; Political science; Political history; Law; Social capital; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.02091299,0.00128897,0.002470701,0.01750217,0.003116041,0.009882575,0.002365201,0.002956861,0.007113311],"category_scores_gemma":[0.06217827,0.0009274755,0.001674468,0.01736239,0.01177268,0.01508703,0.005365998,0.005081789,0.002401597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003444827,"about_ca_system_score_gemma":0.005391181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003127537,"about_ca_topic_score_gemma":0.002962585,"domain_scores_codex":[0.9810524,0.01264996,0.0006446316,0.001912168,0.003452328,0.0002885409],"domain_scores_gemma":[0.9176907,0.06780912,0.002736063,0.007073522,0.003668507,0.001022139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00003426616,0.00003371307,0.001882254,0.0006206232,0.0001447376,0.0001153528,0.00174269,0.003328696,0.0002276078,0.8992478,0.008882686,0.08373965],"study_design_scores_gemma":[0.00001213024,0.00002048584,0.0008065145,0.0003479066,0.00002788536,0.0001284085,0.0005338789,0.0108952,0.0001742042,0.9058471,0.08116614,0.00004010638],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003678127,0.01345037,0.9433354,0.01179884,0.0009390237,0.0002449307,0.001071343,0.0004420182,0.02504003],"genre_scores_gemma":[0.1470815,0.02966089,0.8012756,0.003203807,0.004087393,0.001555333,0.001452456,0.0006152143,0.01106787],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9824978,"threshold_uncertainty_score":0.1105998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03924390007366405,"score_gpt":0.2621328664517311,"score_spread":0.222888966378067,"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."}}