{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001452532,0.000592712,0.000946195,0.0005102264,0.001813534,0.0000817893,0.002045125,0.000667572,0.0004160315],"category_scores_gemma":[0.0006648799,0.0007214409,0.0008294561,0.0003264298,0.001217951,0.0004866968,0.001083665,0.001738463,0.00004217227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000897466,"about_ca_system_score_gemma":0.001240269,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.09247305,"about_ca_topic_score_gemma":0.03457245,"domain_scores_codex":[0.9946373,0.001543978,0.0004837153,0.001084115,0.001362024,0.0008888603],"domain_scores_gemma":[0.9963734,0.001154999,0.0006205402,0.001011149,0.0004090885,0.0004308884],"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":[0.003156003,0.003218862,0.004240011,0.0008812326,0.001206667,0.0001276942,0.3426939,0.01428288,0.0004815756,0.5592797,0.01518162,0.05524979],"study_design_scores_gemma":[0.003025478,0.001394175,0.01889424,0.001627203,0.0005878313,0.000008060594,0.04348532,0.05384156,0.0001450132,0.7130383,0.1607039,0.00324901],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5470663,0.001684868,0.01272201,0.01197172,0.002100143,0.003421729,0.004778517,0.001192367,0.4150624],"genre_scores_gemma":[0.9715855,0.007513978,0.01382355,0.0007722626,0.0008700548,0.000002476121,0.0005493221,0.00006992499,0.004812988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4245192,"threshold_uncertainty_score":0.9995236,"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."}}