{"id":"W3122101082","doi":"10.7910/dvn/e3x2no","title":"Does rising income inequality affect mortality rates in advanced economies?","year":2017,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Global Health Care Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Economic inequality; Economics; Inequality; Demographic economics; Income inequality metrics; Population; Income distribution; Mortality rate; Gini coefficient; Affect (linguistics); Econometrics; Demography; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009147113,0.0002015713,0.0004522685,0.001949838,0.0001997724,0.0009743167,0.0004110889,0.0004019557,0.00272766],"category_scores_gemma":[0.005277958,0.0001183697,0.0005600936,0.004160898,0.0003395,0.0007889498,0.0009448401,0.0008233833,0.0008580192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006599831,"about_ca_system_score_gemma":0.000587441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04648743,"about_ca_topic_score_gemma":0.0412911,"domain_scores_codex":[0.9994999,0.0001236354,0.0000591685,0.0001006106,0.0001030232,0.0001135543],"domain_scores_gemma":[0.9979355,0.0004620025,0.0009666636,0.0001656221,0.000303014,0.0001671445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001118175,0.00003858231,0.9545036,0.0001869158,0.0001967843,0.0001302861,0.0002533669,0.001745433,0.00007342291,0.002150706,0.02936112,0.01124795],"study_design_scores_gemma":[0.00002113916,0.00001626406,0.9757794,0.0001079714,0.0000541712,0.00005739801,0.0004846825,0.0008503697,0.0001110205,0.0006093399,0.02189589,0.00001227574],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.6413414,0.007722251,0.0007405101,0.009460363,0.0002194311,0.00004930506,0.3267066,0.0001011531,0.0136589],"genre_scores_gemma":[0.8300939,0.004556123,0.0003955626,0.0008213189,0.0002450849,0.00009168457,0.1616497,0.00002970502,0.002116798],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.04648743,"threshold_uncertainty_score":0.09243369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06701148675155304,"score_gpt":0.4634012373122394,"score_spread":0.3963897505606863,"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."}}