{"id":"W3122063532","doi":"","title":"Calculating a Standard Error for the Gini Coefficient: Some Further Results","year":2002,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Economic Policies and Impacts","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Jackknife resampling; Standard error; Gini coefficient; Mathematics; Statistics; Regression; Econometrics; Measure (data warehouse); Inequality; Computer science; Economic inequality; Data mining; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005213392,0.0005451349,0.001254788,0.0007405629,0.0004784733,0.0005411979,0.001429745,0.0006324535,0.0002633281],"category_scores_gemma":[0.001967945,0.0005456901,0.0005670714,0.0001839873,0.0004723894,0.000222295,0.00112609,0.00169926,0.0001325416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001875626,"about_ca_system_score_gemma":0.0002572692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006483919,"about_ca_topic_score_gemma":0.0003432656,"domain_scores_codex":[0.9943954,0.0000919808,0.002125177,0.001539927,0.0001045902,0.001742905],"domain_scores_gemma":[0.9950885,0.001555753,0.0009967439,0.001957692,0.00009900719,0.0003023228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001917651,0.0009679468,0.008591248,0.001218779,0.001758463,0.00003660146,0.01693909,0.5392107,0.000018535,0.1756265,0.01272363,0.2409908],"study_design_scores_gemma":[0.003046618,0.0002907265,0.003238199,0.0002027298,0.00001389048,0.000007584902,0.001130206,0.3596264,0.00002536972,0.02489452,0.6064793,0.001044477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4410011,0.01040534,0.0003892698,0.02279042,0.006241826,0.009148979,0.02146059,0.0002508862,0.4883116],"genre_scores_gemma":[0.9746984,0.0117937,0.0006724945,0.0007633249,0.001216352,0.000706243,0.000127555,0.0002242792,0.009797659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5937557,"threshold_uncertainty_score":0.9996995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09833451742380857,"score_gpt":0.3289821800767242,"score_spread":0.2306476626529156,"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."}}