{"id":"W1990736050","doi":"10.1016/j.jpubeco.2010.08.003","title":"Welfare rankings from multivariate data, a nonparametric approach","year":2010,"lang":"en","type":"article","venue":"Journal of Public Economics","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Economic and Social Research Council","keywords":"Social planner; Nonparametric statistics; Data envelopment analysis; Weighting; Economics; Welfare; Econometrics; Multivariate statistics; Incentive; Social Welfare; Measure (data warehouse); Public economics; Computer science; Microeconomics; Mathematics; Statistics; Data mining","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":[0.005350137,0.0001192269,0.0003526253,0.0002495543,0.0003789949,0.000466594,0.001508043,0.0002092685,0.000554918],"category_scores_gemma":[0.002073827,0.0001072529,0.000132494,0.0002537546,0.0001701299,0.001946295,0.0001701751,0.0006356307,0.0000245954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001448377,"about_ca_system_score_gemma":0.0005109858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004570007,"about_ca_topic_score_gemma":0.002204534,"domain_scores_codex":[0.9983374,0.0002097479,0.0006723705,0.0002252878,0.0002147919,0.0003403925],"domain_scores_gemma":[0.9978502,0.0002741356,0.0008508865,0.0004824839,0.0002601794,0.0002821333],"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":[0.0001424756,0.001548094,0.1648872,0.00003476933,0.0007225532,0.00001606973,0.01820531,0.00007335419,0.0002291494,0.6690638,0.01502165,0.1300556],"study_design_scores_gemma":[0.00142134,0.00004508208,0.04306338,0.000005290445,0.00005041367,0.00001073965,0.003676518,0.003929385,0.00001554611,0.01101453,0.9364514,0.0003163944],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9485453,0.00007797711,0.002076612,0.006939496,0.00318829,0.000117905,0.0001257572,0.00002083956,0.03890776],"genre_scores_gemma":[0.9848664,0.0001152958,0.0123481,0.000363032,0.002088856,0.00000101576,0.00002976818,0.00001385224,0.000173637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9214298,"threshold_uncertainty_score":0.6908514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08000601884022815,"score_gpt":0.31664299404618,"score_spread":0.2366369752059519,"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."}}