{"id":"W4391821289","doi":"10.1007/s10888-023-09600-x","title":"Identification-robust methods for comparing inequality with an application to regional disparities","year":2024,"lang":"en","type":"article","venue":"The Journal of Economic Inequality","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; MacEwan University; McGill University; Center for Interuniversity Research and Analysis on Organizations","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Econometrics; Inference; Statistical inference; Mathematics; Sampling distribution; Context (archaeology); Inequality; Null hypothesis; Statistical hypothesis testing; Statistics; Generalized entropy index; Identification (biology); Sample (material); Sample size determination; Computer science; Artificial intelligence; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02105593,0.0001486977,0.0003851469,0.0001107015,0.0005753345,0.0002724742,0.0006558094,0.00008149077,0.00003980655],"category_scores_gemma":[0.0003090061,0.0001050607,0.0001336539,0.0001679941,0.0002398485,0.0008230924,0.00004412591,0.0002407489,0.00001451988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005057941,"about_ca_system_score_gemma":0.0004531221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003421292,"about_ca_topic_score_gemma":0.003621028,"domain_scores_codex":[0.9968446,0.001394432,0.001014192,0.0002255075,0.0002293496,0.0002919091],"domain_scores_gemma":[0.9972208,0.001390359,0.0005181853,0.000391039,0.0003008947,0.0001786793],"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":[0.000980544,0.0001859348,0.01032422,0.000234968,0.0002321287,6.222061e-7,0.03125644,0.005790109,0.0006446369,0.9324908,0.003759748,0.01409987],"study_design_scores_gemma":[0.002389465,0.001459997,0.09746204,0.0004588086,0.0008284377,0.000065748,0.044534,0.08364102,0.002963957,0.5356854,0.2285233,0.001987871],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4751254,0.0001935813,0.5165877,0.006022634,0.0007080324,0.0004620854,0.00003138049,0.00004271447,0.0008264904],"genre_scores_gemma":[0.9905276,0.00005750851,0.007434346,0.0005283153,0.00121059,0.00003276666,0.00001109528,0.00001791729,0.0001798126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5154023,"threshold_uncertainty_score":0.7297606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1498374738602279,"score_gpt":0.4284993939287144,"score_spread":0.2786619200684866,"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."}}