{"id":"W4409664639","doi":"10.31235/osf.io/4qbs7_v1","title":"Asset Poverty and Material Hardship in South Korea","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Asian Industrial and Economic Development","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Poverty; Asset (computer security); Business; Economics; Economic growth; Computer science; Computer security","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003080895,0.0001671313,0.0001377437,0.0009116281,0.0003869702,0.000538434,0.0001239821,0.0001336662,0.002511912],"category_scores_gemma":[0.000986942,0.0001048077,0.0002201968,0.00083114,0.0003811019,0.0005358837,0.00116858,0.0003449245,0.00007313214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002926289,"about_ca_system_score_gemma":0.0003886257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0061269,"about_ca_topic_score_gemma":0.009118771,"domain_scores_codex":[0.9998447,0.00004837205,0.00001503188,0.00001883237,0.00002010679,0.00005291419],"domain_scores_gemma":[0.9993927,0.00007767112,0.0002806624,0.00002208414,0.00005619723,0.0001705853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006879565,0.0000778334,0.9886679,0.00004554154,0.00005791837,0.0004661341,0.001477594,0.0001682611,0.0002021746,0.0008089763,0.0002519627,0.007706797],"study_design_scores_gemma":[0.000002714544,0.0000497701,0.9939381,0.00003580453,0.00001676077,0.0002716144,0.004419448,0.0002066523,0.00005183412,0.0004580391,0.0005442802,0.000004887339],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991167,0.000125577,0.00002833931,0.0001157266,0.000002040691,0.000002155757,0.00004422801,4.04456e-7,0.0005648881],"genre_scores_gemma":[0.9997434,0.0001066558,0.00001652596,0.00001239362,0.000001203009,0.000001463963,0.0000258223,2.366931e-7,0.00009234868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0061269,"threshold_uncertainty_score":0.01218247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0736947855832897,"score_gpt":0.3055303713773536,"score_spread":0.2318355857940639,"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."}}