{"id":"W3035045387","doi":"10.1057/s42214-020-00057-7","title":"Income divergence and global connectivity of U.S. urban regions","year":2020,"lang":"en","type":"article","venue":"Journal of International Business Policy","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; University of Toronto","keywords":"Economics; Divergence (linguistics); Per capita income; Convergence (economics); Distribution (mathematics); Demographic economics; Position (finance); Financial crisis; Panel data; Income distribution; Economic geography; Development economics; Economic growth; Inequality; Econometrics; Macroeconomics; Finance","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":[],"consensus_categories":[],"category_scores_codex":[0.0003377274,0.0001026961,0.0001558955,0.001317189,0.0004219926,0.001045981,0.0002423507,0.0002194222,0.003325221],"category_scores_gemma":[0.003062127,0.00008102001,0.0001766026,0.002909553,0.0007340442,0.001080942,0.001447549,0.000398865,0.0002246784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006939354,"about_ca_system_score_gemma":0.000381993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03075886,"about_ca_topic_score_gemma":0.05665915,"domain_scores_codex":[0.9997954,0.0000668443,0.00001422722,0.00004391731,0.00002413305,0.00005565998],"domain_scores_gemma":[0.9985108,0.0004729068,0.0004839645,0.00008380715,0.0002771792,0.0001713077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001336091,0.00003024749,0.9774727,0.00001946089,0.00007355614,0.0001445768,0.001767762,0.002347554,0.000198634,0.006647583,0.00151404,0.009650307],"study_design_scores_gemma":[0.000006162009,0.00001949471,0.9887974,0.00002360822,0.00002428246,0.00006959007,0.004661736,0.001782432,0.00006842675,0.002969097,0.0015717,0.000006024159],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956504,0.0001652118,0.000111989,0.0003150172,0.000003811417,0.000002286156,0.0004654569,0.000003944107,0.003281908],"genre_scores_gemma":[0.999612,0.00004916394,0.00002752351,0.00001186296,0.000002682708,0.00000115881,0.0001875433,0.000001551447,0.0001063711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03075886,"threshold_uncertainty_score":0.06115967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02944712173042215,"score_gpt":0.247089207667429,"score_spread":0.2176420859370069,"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."}}