{"id":"W2072648216","doi":"10.1111/j.1468-0084.2004.00087.x","title":"Calculating a Standard Error for the Gini Coefficient: Some Further Results: Reply","year":2004,"lang":"en","type":"article","venue":"Oxford Bulletin of Economics and Statistics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University; University of Northern British Columbia","funders":"","keywords":"Gini coefficient; Standard error; Statistics; Econometrics; Mathematics; Economics; Inequality; Mathematical analysis; Economic inequality","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":[],"consensus_categories":[],"category_scores_codex":[0.0009735413,0.0002159476,0.0005592365,0.00009645811,0.000229492,0.00009810027,0.0002190816,0.00008526301,0.0001478357],"category_scores_gemma":[0.0003690995,0.0002070669,0.0001213805,0.00003508166,0.0001936304,0.00005812628,0.00006642296,0.0001200437,0.00003450692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001012012,"about_ca_system_score_gemma":0.00003418501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008418869,"about_ca_topic_score_gemma":0.00006468943,"domain_scores_codex":[0.9979834,0.000008386319,0.001166933,0.0004287925,0.00001747182,0.0003950003],"domain_scores_gemma":[0.9982482,0.0004396318,0.0007731082,0.0004079022,0.00002563447,0.000105489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000368452,0.00005230265,0.0003850537,0.00006591557,0.0001613483,0.000001241816,0.0009683336,0.08351918,6.453371e-7,0.9045627,0.00621048,0.003704406],"study_design_scores_gemma":[0.002785904,0.0003601138,0.0007154159,0.00002061557,0.00002567291,0.00000718765,0.0002275463,0.06062916,0.0000207234,0.1246692,0.8102146,0.0003239202],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5387673,0.006838713,0.2875423,0.06153968,0.002374417,0.003031243,0.08170008,0.00009216071,0.01811408],"genre_scores_gemma":[0.928887,0.003768343,0.06261802,0.002951704,0.0003415866,0.00005303033,0.0001589896,0.00007891226,0.001142396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8040041,"threshold_uncertainty_score":0.8443943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0499561769739803,"score_gpt":0.2401808986229503,"score_spread":0.19022472164897,"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."}}