{"id":"W4402100011","doi":"10.1093/jrsssa/qnae080","title":"Mapping socio-economic status using mixed data: a hierarchical Bayesian approach","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series A (Statistics in Society)","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Fonds de recherche du Québec – Nature et technologies; Institut de Valorisation des Données; Natural Sciences and Engineering Research Council of Canada; Wellcome Trust","keywords":"Econometrics; Bayesian probability; Statistics; Hierarchical database model; Multilevel model; Geography; Multivariate statistics; Index (typography); Small area estimation; Computer science; Mathematics; Data mining","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":[0.01689262,0.001333362,0.002791509,0.006142477,0.00184621,0.004162088,0.005356166,0.002353673,0.00385957],"category_scores_gemma":[0.04280111,0.002015354,0.004173576,0.005208671,0.001748882,0.003377184,0.003771685,0.002924908,0.0008171145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002891348,"about_ca_system_score_gemma":0.002175093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03527327,"about_ca_topic_score_gemma":0.03917617,"domain_scores_codex":[0.9886402,0.008063318,0.0004191996,0.001694614,0.0008217461,0.0003608518],"domain_scores_gemma":[0.9736176,0.02092181,0.001880445,0.001549941,0.001607607,0.0004225988],"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":[0.0003111684,0.0003459529,0.05051794,0.000681489,0.001831254,0.0009371691,0.001945095,0.5074727,0.001067741,0.2426513,0.00603784,0.1862003],"study_design_scores_gemma":[0.00003592272,0.00004849801,0.003434544,0.0001354842,0.0001663704,0.00009741229,0.0001572095,0.8472947,0.0001804252,0.1458307,0.002561603,0.00005728327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01127397,0.0005214954,0.985828,0.0006235766,0.00003655074,0.0001202313,0.0006413993,0.0002004746,0.0007542187],"genre_scores_gemma":[0.3280333,0.0009302218,0.6648462,0.0004331295,0.0002071437,0.0009691275,0.002557741,0.0001314341,0.001891677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03527327,"threshold_uncertainty_score":0.08933783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08501493187793888,"score_gpt":0.2606655363507903,"score_spread":0.1756506044728514,"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."}}