{"id":"W3212580031","doi":"10.1016/j.seps.2021.101195","title":"Is poverty predictable with machine learning? A study of DHS data from Kyrgyzstan","year":2021,"lang":"en","type":"article","venue":"Socio-Economic Planning Sciences","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Centre Hospitalier Universitaire de Sherbrooke","funders":"Fonds de Recherche du Québec - Santé; Xingjiang Uighur Autonomous Region Talent Project; Xinjiang University","keywords":"Machine learning; Poverty; A priori and a posteriori; Computer science; Artificial intelligence; Variable (mathematics); Variables; Key (lock); Asset (computer security); Econometrics; Mathematics; 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":[0.00256105,0.0002411622,0.0005268225,0.0009359537,0.001058327,0.001171756,0.0008676288,0.0003780183,0.00129948],"category_scores_gemma":[0.009086359,0.0002435747,0.0004560328,0.00309335,0.0009115413,0.001038342,0.001013674,0.0009091114,0.0002194565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001907216,"about_ca_system_score_gemma":0.00215565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2309331,"about_ca_topic_score_gemma":0.2179843,"domain_scores_codex":[0.9987904,0.0006676365,0.00006009805,0.0001279652,0.00008675108,0.0002670789],"domain_scores_gemma":[0.9903238,0.006198422,0.001525261,0.0007244254,0.0009180311,0.0003101103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006610981,0.00007428249,0.9898909,0.00002390731,0.000114455,0.0001278278,0.0007936507,0.002107188,0.00006566056,0.0008705421,0.001295247,0.004570186],"study_design_scores_gemma":[0.00001605487,0.00004237804,0.9707781,0.0000354904,0.00007877998,0.00008545543,0.007469716,0.01830414,0.0001628282,0.0007011399,0.002311094,0.00001487981],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979796,0.0001244116,0.0001294016,0.0006415914,0.000003788732,0.000004482638,0.0008151923,0.000005857411,0.0002956179],"genre_scores_gemma":[0.9986584,0.00007985937,0.0001050906,0.00003925243,0.000006635488,0.000004298878,0.000984443,0.000002697627,0.000119258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2309331,"threshold_uncertainty_score":0.4591779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09070523423214912,"score_gpt":0.3559220115813397,"score_spread":0.2652167773491906,"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."}}