{"id":"W4283789493","doi":"10.1002/cjs.11708","title":"Pseudo empirical likelihood inference for nonprobability survey samples","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Waterloo; Institute for Clinical Evaluative Sciences; SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Nonprobability sampling; Estimator; Inference; Survey sampling; Point estimation; Statistical inference; Statistics; Sampling (signal processing); Computer science; Survey data collection; Range (aeronautics); Econometrics; Survey research; Mathematics; Artificial intelligence; Engineering; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002762552,0.000170607,0.0004695362,0.0001685132,0.0004128235,0.00008158244,0.0004402879,0.00005557876,0.001143525],"category_scores_gemma":[0.02398455,0.00016223,0.00008540485,0.0002557435,0.0001755656,0.00005829287,0.00003998202,0.0004541895,0.000002480306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003271115,"about_ca_system_score_gemma":0.003147558,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002534166,"about_ca_topic_score_gemma":0.02664577,"domain_scores_codex":[0.9976292,0.0005577045,0.0007913162,0.0001920118,0.0003400749,0.0004896407],"domain_scores_gemma":[0.9881598,0.009653427,0.0004097677,0.0002445654,0.0007811368,0.0007513494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000122522,0.0001415061,0.1025084,0.0001892873,0.0000988524,0.0001507593,0.0009153411,0.00001694825,0.00001596229,0.7383217,0.1044468,0.05307191],"study_design_scores_gemma":[0.0003987096,0.000560043,0.05096393,0.00001905511,0.00006096693,0.00006914742,0.0001529717,0.0006590227,0.00001059319,0.9415557,0.005345802,0.0002039919],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009113679,0.00004168328,0.9767664,0.0002626836,0.0005737562,0.0002283479,0.01289284,0.000006496406,0.0001141395],"genre_scores_gemma":[0.185725,0.000003578368,0.8138738,0.0002106794,0.00008572263,0.00001632785,0.00003544085,0.00002395733,0.0000254507],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2032341,"threshold_uncertainty_score":0.9997696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1647527977897741,"score_gpt":0.3922188911423007,"score_spread":0.2274660933525266,"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."}}