{"id":"W2372921239","doi":"","title":"Economic Growth in Countries with Different Languages since 1990s:Comparison and Implications——The Case of Major English-Speaking Countries and Non-English-Speaking Countries","year":2007,"lang":"en","type":"article","venue":"Journal of Guizhou College of Finance and Economics","topic":"Second Language Learning and Teaching","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Restructuring; Economics; Per capita; Productivity; Developing country; Unemployment; Economic restructuring; Developed country; Unemployment rate; Development economics; Demographic economics; Labour economics; Economic growth; Sociology; Population","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001118711,0.0002518009,0.0003940783,0.004218235,0.0008037274,0.002569803,0.0003420341,0.000458484,0.002774123],"category_scores_gemma":[0.002724889,0.0001429141,0.0009467888,0.006512323,0.00071532,0.002009,0.001822917,0.0008610934,0.0003145909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001216453,"about_ca_system_score_gemma":0.0009271337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0398134,"about_ca_topic_score_gemma":0.05292982,"domain_scores_codex":[0.9993449,0.0001177225,0.00005563474,0.00008029516,0.00007237872,0.0003289691],"domain_scores_gemma":[0.9973421,0.0005002791,0.0008357245,0.00007877023,0.000705259,0.0005379915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000423886,0.00009313228,0.9559488,0.0002076328,0.0003155595,0.002157005,0.002163142,0.001262193,0.0001665499,0.006101881,0.004142633,0.02701758],"study_design_scores_gemma":[0.00002177959,0.00006885816,0.9833732,0.0001407502,0.0001096886,0.0005506291,0.00792935,0.0006626446,0.0001393647,0.0006506033,0.006335902,0.00001723354],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745448,0.007223501,0.0001038008,0.002283924,0.00009523298,0.00001251989,0.001385487,0.0000111973,0.01433961],"genre_scores_gemma":[0.9957269,0.002429453,0.00005223635,0.0001092532,0.00003116359,0.00000506316,0.001081681,0.000003531555,0.0005606476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0398134,"threshold_uncertainty_score":0.07916331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008131520534960582,"score_gpt":0.2261731513523781,"score_spread":0.2180416308174176,"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."}}