{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002380064,0.0001729636,0.0004014346,0.00006637288,0.0014656,0.000237765,0.001249762,0.00009493333,0.001025983],"category_scores_gemma":[0.0002750066,0.0001451222,0.00004872282,0.0002967645,0.000751323,0.0009383143,0.0003321756,0.0002504152,0.00003535147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001631617,"about_ca_system_score_gemma":0.001163881,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04028861,"about_ca_topic_score_gemma":0.006647636,"domain_scores_codex":[0.9973425,0.0004514772,0.0004096226,0.0007768224,0.0005482286,0.0004713678],"domain_scores_gemma":[0.998481,0.0004662169,0.000341367,0.0005099082,0.00007340523,0.0001280606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002865384,0.0001717095,0.9397082,0.000006882944,0.00008594381,0.00001011142,0.05356692,0.0004896476,0.00003434501,0.000745365,0.005023607,0.0001286645],"study_design_scores_gemma":[0.002641975,0.001131585,0.127119,0.0001740722,0.0001809111,0.000004335031,0.7820125,0.01471666,0.0002384952,0.004512411,0.06627326,0.0009947417],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9624821,0.0004955365,0.00004720088,0.00100771,0.0004341292,0.0001782306,0.0004319152,0.00007083213,0.03485236],"genre_scores_gemma":[0.9972835,0.00005640546,0.0003938289,0.0004676312,0.0002300154,0.000005009101,0.00007782967,0.00001042691,0.001475396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8125891,"threshold_uncertainty_score":0.9998872,"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."}}