{"id":"W4385622836","doi":"10.21203/rs.3.rs-2082385/v3","title":"Measuring income inequality via percentile relativities","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Index (typography); Percentile; Inequality; Income distribution; Viewpoints; Econometrics; Economic inequality; Economics; Population; Mathematics; Statistics; Sociology; Demography; Computer science; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002586853,0.0006437246,0.0006320589,0.004689866,0.0008139191,0.00373635,0.0006088673,0.0007123699,0.00798455],"category_scores_gemma":[0.02209653,0.0003244544,0.0004481167,0.005807742,0.001888743,0.004617821,0.003161461,0.001427626,0.0008883892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008387203,"about_ca_system_score_gemma":0.0005835198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002630165,"about_ca_topic_score_gemma":0.001850465,"domain_scores_codex":[0.9976339,0.0009920986,0.00009381273,0.0004273509,0.0006495445,0.0002031959],"domain_scores_gemma":[0.993926,0.003504892,0.0007897051,0.001062265,0.0004871816,0.0002299817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000208356,0.0001272829,0.07606845,0.0001639152,0.0001493126,0.00007873594,0.00153327,0.01757833,0.003047669,0.7174722,0.006452392,0.1771201],"study_design_scores_gemma":[0.00001639465,0.0001148845,0.08819635,0.0001101527,0.00008194245,0.0001826356,0.00225773,0.0752454,0.004604479,0.8144774,0.01463396,0.00007871105],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4803259,0.00184083,0.3936675,0.002183265,0.0004369825,0.00009529018,0.003989629,0.001384425,0.1160763],"genre_scores_gemma":[0.9634304,0.0005355242,0.03216558,0.00006288813,0.0002077428,0.00006060918,0.0007707952,0.0001391365,0.002627171],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00798455,"threshold_uncertainty_score":0.02671099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.291837758886198,"score_gpt":0.454250411558975,"score_spread":0.162412652672777,"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."}}