{"id":"W1557857476","doi":"","title":"Revisiting Recent Trends in Canadian After-Tax Income Inequality Using Census Data","year":2006,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Fiscal Policy and Economic Growth","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Economics; Economic inequality; Inequality; Income distribution; Survey data collection; Distribution (mathematics); Income inequality metrics; State income tax; Demographic economics; Income tax; Public economics; Labour economics; Tax reform; Statistics; 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":[0.001678314,0.0003084983,0.0003749354,0.006268355,0.001946223,0.00219786,0.0009089425,0.0002304617,0.002416352],"category_scores_gemma":[0.008342687,0.0001757181,0.0004362541,0.01928812,0.0006418836,0.0008430897,0.000945415,0.0007667201,0.0001890657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02619186,"about_ca_system_score_gemma":0.02513772,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9963043,"about_ca_topic_score_gemma":0.9977043,"domain_scores_codex":[0.9985672,0.00006909935,0.00006534738,0.0001488149,0.0007478023,0.0004016844],"domain_scores_gemma":[0.9916421,0.0007309814,0.001076495,0.0003663727,0.005672022,0.0005121399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001117936,0.0000244824,0.9332728,0.0001533684,0.0001158359,0.0001498668,0.003036694,0.001225207,0.0003369371,0.003205721,0.01327321,0.04509401],"study_design_scores_gemma":[0.000002206645,0.000004510684,0.9883019,0.00005175756,0.00002118843,0.0000173241,0.000969869,0.0008038565,0.000162373,0.0001278516,0.00952592,0.00001122711],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9214257,0.005249884,0.00144224,0.004917443,0.00009735776,0.00004593091,0.04668975,0.00008662482,0.02004507],"genre_scores_gemma":[0.9797861,0.002296991,0.0007098195,0.0002354494,0.00003495394,0.000012752,0.01494793,0.00002223463,0.001953798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02619186,"threshold_uncertainty_score":0.1900361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.139805191358293,"score_gpt":0.3471086968474624,"score_spread":0.2073035054891693,"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."}}