{"id":"W2097172763","doi":"","title":"Some Notes on Sample Selection Models","year":2009,"lang":"en","type":"preprint","venue":"Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Selection (genetic algorithm); Econometrics; Sample (material); Model selection; Parametric statistics; Estimation; Semiparametric model; Selection bias; Computer science; Parametric model; Indirect Inference; Economics; Nonparametric statistics; Machine learning; Artificial intelligence; Statistics; Mathematics","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":[0.01717293,0.001666367,0.00182535,0.002089744,0.001575101,0.003322246,0.002845281,0.00417538,0.04220794],"category_scores_gemma":[0.06140451,0.001087719,0.002452433,0.005518171,0.00306647,0.004354689,0.002105719,0.008165114,0.006879238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002813073,"about_ca_system_score_gemma":0.002081747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007019931,"about_ca_topic_score_gemma":0.006555754,"domain_scores_codex":[0.9899877,0.006644304,0.0005560382,0.0008644176,0.001660333,0.0002871801],"domain_scores_gemma":[0.954686,0.0400946,0.001094742,0.002250041,0.001611616,0.0002630783],"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":[0.00002515462,0.00005001143,0.0006761525,0.0002495471,0.00005568595,0.0001869399,0.0002569623,0.005849715,0.0000717214,0.8685603,0.08675352,0.03726444],"study_design_scores_gemma":[0.00002671627,0.00002002235,0.0004286544,0.0002157477,0.00002405344,0.00006423225,0.0000316049,0.006445924,0.00005601923,0.9004962,0.09216776,0.00002294252],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002516308,0.02561817,0.765821,0.07288335,0.007163069,0.0004775793,0.006781731,0.0006183283,0.1181204],"genre_scores_gemma":[0.1339634,0.08349514,0.4495433,0.06523348,0.04944943,0.005521229,0.01171838,0.001614725,0.199461],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04220794,"threshold_uncertainty_score":0.1411996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05547128483463011,"score_gpt":0.2796067060477971,"score_spread":0.224135421213167,"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."}}