{"id":"W2980548856","doi":"10.1080/01621459.2019.1677241","title":"Doubly Robust Inference With Nonprobability Survey Samples","year":2019,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":154,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Nonprobability sampling; Robustness (evolution); Statistics; Sample (material); Survey sampling; Population; Inference; Computer science; Variance (accounting); Statistical inference; Sampling (signal processing); Econometrics; Mathematics; Artificial intelligence","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00261523,0.0001518999,0.0006025266,0.00004061334,0.00007228447,0.00007417497,0.0003283824,0.00004375422,0.0001802331],"category_scores_gemma":[0.021722,0.00008496528,0.00008022584,0.0004075035,0.0001696363,0.0001105342,0.00005859608,0.000424818,0.00001646599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003585126,"about_ca_system_score_gemma":0.0002128093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002674176,"about_ca_topic_score_gemma":0.0001519088,"domain_scores_codex":[0.997013,0.001112381,0.0006177557,0.0001661977,0.0008171374,0.0002735779],"domain_scores_gemma":[0.9803104,0.01673848,0.001829888,0.0002647773,0.0007447523,0.000111649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003148579,0.0001781192,0.8885725,0.00004067081,0.0001309878,0.000003320981,0.00008710725,0.00003887816,0.0001034122,0.09642671,0.002394399,0.01170905],"study_design_scores_gemma":[0.0003213887,0.0004008834,0.7655349,0.00004798995,0.00007124459,0.000007309991,0.00003872969,0.0003012141,0.00003879488,0.2330485,0.00007341358,0.000115557],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2790999,0.000002535395,0.7196538,0.0004447644,0.0001606022,0.0001481657,0.0001856414,0.000009353686,0.0002952463],"genre_scores_gemma":[0.5573253,0.000002863211,0.4424158,0.0001120757,0.00004598188,0.000001692389,0.000001848847,0.00001115862,0.00008327447],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2782255,"threshold_uncertainty_score":0.9865184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06062457211919049,"score_gpt":0.3571311995777698,"score_spread":0.2965066274585793,"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."}}