{"id":"W2738193654","doi":"10.1111/rssa.12306","title":"Poverty Mapping in Small Areas Under a Twofold Nested Error Regression Model","year":2017,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series A (Statistics in Society)","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Research Council Canada","keywords":"Estimator; Small area estimation; Statistics; Econometrics; Mean squared error; Mathematics; Poverty; Estimation; Domain (mathematical analysis); Regression; Monte Carlo method; Standard error; Economics","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":[0.01221509,0.0009083863,0.00200089,0.001279088,0.000440876,0.001624351,0.00278715,0.001284106,0.002726832],"category_scores_gemma":[0.02718835,0.0006398814,0.001619843,0.001371467,0.001329217,0.001880506,0.002783601,0.001449671,0.0004795292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007993128,"about_ca_system_score_gemma":0.0008209904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01185117,"about_ca_topic_score_gemma":0.00486236,"domain_scores_codex":[0.9924095,0.004906209,0.000252753,0.001292533,0.000623598,0.0005154994],"domain_scores_gemma":[0.9846683,0.009727743,0.002367882,0.001552057,0.001263598,0.0004203449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004583098,0.0002082069,0.03204459,0.0001505094,0.0002545209,0.0007155513,0.0007773127,0.8531516,0.001463783,0.07756293,0.0009953295,0.03221739],"study_design_scores_gemma":[0.00001759043,0.00006014784,0.003956309,0.0000167115,0.00002555491,0.00005392553,0.00008505744,0.9790203,0.0001914953,0.0162129,0.000337036,0.00002302392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3675326,0.0003468705,0.6284643,0.0005421605,0.00004199534,0.0001215808,0.0005367326,0.0002619627,0.002151788],"genre_scores_gemma":[0.9090634,0.0002907194,0.08304223,0.00009546136,0.00003473811,0.0002733289,0.000675427,0.00007417096,0.006450412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01221509,"threshold_uncertainty_score":0.06460035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06765211363078552,"score_gpt":0.2678716173929229,"score_spread":0.2002195037621374,"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."}}