{"id":"W6946248185","doi":"10.34989/swp-2025-17","title":"Correcting Selection Bias in a Non-Probability Two-Phase Payment Survey","year":2025,"lang":"en","type":"article","venue":"Bank of Canada Research","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Selection (genetic algorithm); Selection bias; Calibration; Variance (accounting); Payment; Sample (material); Estimation; Sample size determination","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002893024,0.00006888787,0.0001361759,0.00009560979,0.00007634577,0.000009711242,0.0001735626,0.00008900664,0.00001560622],"category_scores_gemma":[0.002928374,0.00006285596,0.00002255415,0.000508604,0.000126379,9.956834e-7,0.0001142951,0.0002097013,2.417414e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001461666,"about_ca_system_score_gemma":0.002052951,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5409441,"about_ca_topic_score_gemma":0.9186438,"domain_scores_codex":[0.9984698,0.0004136925,0.0002173198,0.00025978,0.0003105455,0.0003288121],"domain_scores_gemma":[0.9992356,0.0002304449,0.00003169285,0.0001954932,0.0002478797,0.00005889455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00136742,0.0006028914,0.3759489,0.0002679679,0.0001097955,0.00001964928,0.0001050006,0.0002534958,0.1420905,0.00008370673,0.09160995,0.3875407],"study_design_scores_gemma":[0.004178273,0.001136644,0.4397675,0.0001831347,0.000007198598,0.000008843827,0.0006101857,0.002596012,0.5252525,0.0004115568,0.02553136,0.0003168103],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975837,0.00022783,0.0003597797,0.0003263878,0.0001350152,0.0001650806,0.00001500191,0.000003258637,0.001183891],"genre_scores_gemma":[0.9987976,0.00001044644,0.0002228905,0.00004339026,0.00002389224,0.00002285204,0.00002670177,0.000004035597,0.0008482113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3872239,"threshold_uncertainty_score":0.4621128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09431446954622,"score_gpt":0.4124850552878784,"score_spread":0.3181705857416584,"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."}}