{"id":"W2624915418","doi":"10.1515/snde-2016-0062","title":"Detecting capital market convergence clubs","year":2017,"lang":"en","type":"article","venue":"Studies in Nonlinear Dynamics and Econometrics","topic":"Economic Growth and Development","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Unobservable; Convergence (economics); Economics; Pairwise comparison; Econometrics; Stock market; Capital market; Arbitrage; Financial economics; Mathematics; Macroeconomics; Finance; Statistics; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006308993,0.0001401166,0.0002613986,0.0003141351,0.0004200181,0.0002264175,0.0006617832,0.00005175176,0.00000645853],"category_scores_gemma":[0.000590992,0.0001427864,0.00003485167,0.0001854928,0.0001464137,0.0004584623,0.001084807,0.0001297889,0.00001350654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000172369,"about_ca_system_score_gemma":0.00004131574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004323422,"about_ca_topic_score_gemma":0.0005388597,"domain_scores_codex":[0.9989123,0.00001132878,0.0003167981,0.0003976065,0.00005858496,0.0003033286],"domain_scores_gemma":[0.9990298,0.0001766932,0.0001895475,0.0004704946,0.00005218884,0.0000813475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008282107,0.00005220495,0.8520071,0.0001055738,0.0001045767,0.00004768335,0.001667115,0.00004866097,6.490042e-7,0.02706436,0.0001800104,0.1187138],"study_design_scores_gemma":[0.0005936763,0.00006355518,0.1761453,0.00002907844,0.000004214017,0.00001876909,0.0009975022,0.8146306,0.00001745411,0.006402478,0.000699995,0.0003973382],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9754739,0.001518885,0.009158228,0.0005142647,0.002000905,0.000135564,0.00001170794,0.00003358319,0.01115292],"genre_scores_gemma":[0.9662751,0.002793909,0.03035586,0.0000956458,0.00005988276,0.00001041108,0.000001334907,0.000007447257,0.000400421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.814582,"threshold_uncertainty_score":0.582266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04385248383049429,"score_gpt":0.2799666214877153,"score_spread":0.236114137657221,"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."}}