{"id":"W2143245567","doi":"","title":"Adjustment of unemployment estimates based on small area estimation in Korea","year":2003,"lang":"en","type":"article","venue":"Survey methodology","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Small area estimation; Statistics; Jackknife resampling; Estimator; Econometrics; Estimation; Unemployment; Mean squared error; Metropolitan area; Population; Efficiency; Mathematics; Geography; Economics; Demography; Economic growth","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005900272,0.0001331656,0.0005690483,0.0004092635,0.00002104502,0.000006896238,0.0001344323,0.000103156,0.0005494704],"category_scores_gemma":[0.00434179,0.0001386639,0.00007769171,0.0004018672,0.00004446768,0.00003356538,0.00001636306,0.00008930773,0.0000637676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000600551,"about_ca_system_score_gemma":0.00002319984,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00720665,"about_ca_topic_score_gemma":0.002074045,"domain_scores_codex":[0.9980401,0.0007692017,0.000617237,0.000335721,0.00003021204,0.0002075206],"domain_scores_gemma":[0.9968894,0.002401123,0.0002950031,0.0003408503,0.00003047908,0.00004319556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001214241,0.0002695146,0.8946408,0.00003201012,0.00006464485,0.000002916362,0.0001287865,0.06953047,0.00005010983,0.0314933,0.00006675215,0.003599277],"study_design_scores_gemma":[0.0007080305,0.0002165355,0.8575425,0.00001513404,0.00001485695,8.281948e-7,0.00001484041,0.115398,0.001396479,0.02411754,0.0003703504,0.0002048838],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4263999,0.0005212056,0.5688485,0.0001247781,0.0004443091,0.0002348533,0.0005081372,0.00001912799,0.002899254],"genre_scores_gemma":[0.8806387,0.00003701821,0.1186488,0.0001820621,0.000006775595,0.00002961568,0.0003950066,0.00001387401,0.00004817108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4542388,"threshold_uncertainty_score":0.9994044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3280569702114287,"score_gpt":0.332284060759851,"score_spread":0.004227090548422352,"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."}}