{"id":"W6976743408","doi":"10.6068/dp14ba8766d4450","title":"Trend 1976 - 2011. Statistics Canada. CANSIM: Income, Pensions, Spending and Wealth - Low Income and Inequality | Country: Canada | Table: Gini coefficients of market, total and after-tax income of individuals, where each individual is represented by their adjusted household income, by economic family type | Variable: Two-parent families with children, two earners, Adjusted market income | Units: #, 1976-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-121.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Economic inequality; Official statistics; Gini coefficient; Population; Inequality; Socioeconomic status; Census; Household income; Demographic statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00210738,0.002501058,0.002926854,0.007862321,0.003010345,0.004666066,0.005740002,0.001441082,0.09492102],"category_scores_gemma":[0.01739308,0.001880532,0.002328419,0.03718647,0.0006140312,0.00244992,0.002447418,0.003593312,0.04865661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05238618,"about_ca_system_score_gemma":0.122454,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.993936,"about_ca_topic_score_gemma":0.9916589,"domain_scores_codex":[0.9960029,0.0002545707,0.000438633,0.0004747869,0.001892964,0.0009361984],"domain_scores_gemma":[0.9640269,0.001193834,0.001091165,0.0009253027,0.03119278,0.001570103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002600211,0.000008096674,0.001294084,0.0002599996,0.00002234481,0.000006292042,0.00002217291,0.000111287,0.000008722724,0.0003837245,0.9961119,0.001745313],"study_design_scores_gemma":[0.0002275941,0.00001738171,0.0410462,0.001074674,0.00008521482,0.00003523347,0.0005405228,0.0005544531,0.0002325066,0.00075759,0.9553106,0.0001180811],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006417203,0.00004781388,0.00002291813,0.0001025315,0.00002638875,0.00001794881,0.998803,0.00005396905,0.0008613146],"genre_scores_gemma":[0.0009726295,0.0002910258,0.0003838011,0.0001473084,0.00002027125,0.0001650011,0.9928786,0.000122573,0.005018771],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09492102,"threshold_uncertainty_score":0.3800901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0240634736989799,"score_gpt":0.2466835456711214,"score_spread":0.2226200719721415,"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."}}