{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004947361,0.0001496851,0.001028909,0.0001345651,0.0001593338,0.0001834986,0.0002851016,0.0000550891,0.00005477439],"category_scores_gemma":[0.0002175489,0.000157548,0.0002072303,0.00009601544,0.0001464225,0.000463649,0.0002308275,0.00006567204,0.00002581287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001430068,"about_ca_system_score_gemma":0.00001046551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001094849,"about_ca_topic_score_gemma":0.0002734051,"domain_scores_codex":[0.9986452,0.00000688231,0.0007713541,0.0004081906,0.00001410736,0.0001542404],"domain_scores_gemma":[0.9980865,0.00004223128,0.001060751,0.0007355816,0.00002376614,0.00005115685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009954277,0.00004050179,0.681214,0.001120543,0.0005860952,0.000002072759,0.00003977442,0.00001902937,2.801548e-7,0.1649587,0.00005403298,0.1519551],"study_design_scores_gemma":[0.0005181774,0.00007974556,0.7572313,0.0007026514,0.0003631432,0.000004243328,0.000004826277,0.01068264,0.000006311653,0.03425533,0.1955815,0.0005701443],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.827036,0.1547581,0.002469909,0.002127451,0.0001507871,0.0002714949,0.002831391,0.00000907819,0.01034582],"genre_scores_gemma":[0.6528803,0.3466454,0.0001211413,0.0001352516,0.00003701749,0.000006723185,0.00004337422,0.000006144222,0.0001246887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1955274,"threshold_uncertainty_score":0.6424623,"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."}}