{"id":"W2636275679","doi":"","title":"Digital Divide and Income Inequality: A Spatial Analysis","year":2017,"lang":"en","type":"article","venue":"Review of Economics and Finance","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic inequality; Income distribution; Economics; Income inequality metrics; Inequality; Spillover effect; Demographic economics; Distribution (mathematics); Quantile regression; Comprehensive income; Total personal income; Estimation; Econometrics; Gross income; Public economics; Macroeconomics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.002144929,0.0003812419,0.0007055374,0.002604367,0.0005177342,0.001289442,0.0009104134,0.0006808495,0.007930262],"category_scores_gemma":[0.005218091,0.0002044135,0.001703906,0.004908456,0.0008747997,0.001295664,0.001991161,0.0009639481,0.0005225256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009408416,"about_ca_system_score_gemma":0.0007089765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02730933,"about_ca_topic_score_gemma":0.01211962,"domain_scores_codex":[0.9987801,0.0006825369,0.0000433759,0.0001839997,0.0001512164,0.000158834],"domain_scores_gemma":[0.9966186,0.002020416,0.0006236408,0.0003069745,0.0002962289,0.0001341739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002393244,0.0003339263,0.7085091,0.0002889549,0.001332075,0.00132415,0.001406853,0.1064134,0.0006057226,0.09699161,0.004988907,0.07756589],"study_design_scores_gemma":[0.00003995033,0.0002404782,0.3217655,0.000170747,0.0007388616,0.0003703252,0.003185465,0.6250544,0.0006765,0.03710252,0.01059171,0.00006351082],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9162566,0.002803291,0.06482609,0.00193404,0.00006613062,0.0001345519,0.00225536,0.0001705605,0.01155336],"genre_scores_gemma":[0.9933056,0.0006880914,0.003645296,0.00005221538,0.00003038275,0.00005456242,0.0005915791,0.0000136402,0.001618559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02730933,"threshold_uncertainty_score":0.05430073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04039140052629323,"score_gpt":0.2488036794690055,"score_spread":0.2084122789427123,"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."}}