{"id":"W1977969057","doi":"10.1016/j.strueco.2004.08.001","title":"Output and well-being in industrialized nations in the second half of the 20th century: testing for convergence using fuzzy clustering analysis","year":2004,"lang":"en","type":"article","venue":"Structural Change and Economic Dynamics","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Convergence (economics); Context (archaeology); Economics; Test (biology); Empirical evidence; Econometrics; Per capita income; Per capita; Convergence clubs; Sample (material); Macroeconomics; Geography; Sociology","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":[0.0006001909,0.000148819,0.0004085645,0.0003369227,0.0001586069,0.00006404272,0.0001890385,0.00009691234,0.00001319633],"category_scores_gemma":[0.00008343548,0.0001322533,0.00007969515,0.0003457252,0.00008985522,0.00032173,0.00009127879,0.0001568236,6.810506e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002306185,"about_ca_system_score_gemma":0.00002791126,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004673711,"about_ca_topic_score_gemma":0.03472504,"domain_scores_codex":[0.9988134,0.00002340228,0.0005435951,0.0003542733,0.00001465347,0.0002506645],"domain_scores_gemma":[0.9992489,0.0001308711,0.000366463,0.0002095493,0.00001126347,0.00003300212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002504286,0.000007684574,0.9012394,0.0000859903,0.00007963647,4.693121e-7,0.003276258,0.005219289,0.000008366571,0.08924215,8.177815e-7,0.0008149046],"study_design_scores_gemma":[0.002054913,0.00004880136,0.4536267,0.0000432935,0.00006137145,0.00001506812,0.001196344,0.4429295,0.00002168432,0.09943326,0.0001811079,0.0003879709],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969206,0.0002854709,0.0001182867,0.0005838959,0.000429993,0.0004890601,0.0002037581,0.000004499984,0.0009643852],"genre_scores_gemma":[0.9992478,0.00005066329,0.0004281003,0.00008899611,0.0001122777,0.00002191552,0.00001775379,0.00001095017,0.00002153781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4476127,"threshold_uncertainty_score":0.9828887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0651422165618794,"score_gpt":0.2466984101800674,"score_spread":0.181556193618188,"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."}}