{"id":"W3132461504","doi":"10.1016/j.jeconom.2024.105742","title":"The law of large numbers for large stable matchings","year":2024,"lang":"en","type":"article","venue":"Journal of Econometrics","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Matching (statistics); Mathematics; Estimator; Consistency (knowledge bases); Econometrics; Law of large numbers; Inference; Fraction (chemistry); Inequality; Statistical inference; Statistics; Computer science; Discrete mathematics; Random variable","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":[],"consensus_categories":[],"category_scores_codex":[0.006664064,0.0001214879,0.0005023869,0.0005619737,0.000172886,0.0002137885,0.0003913192,0.00008767698,0.0001391671],"category_scores_gemma":[0.0005397323,0.0001004576,0.0003825917,0.0006718124,0.0000357446,0.0004199149,0.00004439323,0.0002307546,0.00008467278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009842712,"about_ca_system_score_gemma":0.00003915505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002314236,"about_ca_topic_score_gemma":0.00001608116,"domain_scores_codex":[0.9980764,0.00002270825,0.001338162,0.0001657149,0.00004746489,0.0003494885],"domain_scores_gemma":[0.9976127,0.001006834,0.0009892457,0.0002115464,0.00009676763,0.00008293206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000257594,0.000062586,0.002897132,0.0001672156,0.0001867387,0.000002521426,0.0004651913,0.0000349475,0.000004698513,0.9920363,0.003923262,0.0001936876],"study_design_scores_gemma":[0.0006046987,0.0001785699,0.0003560939,0.0000708087,0.00002435851,0.00002617751,0.0005963709,0.0014522,0.00009980998,0.1559776,0.8404685,0.0001447957],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.641218,0.1127629,0.09001398,0.003317725,0.01703514,0.0009781668,0.002698377,0.00008429322,0.1318914],"genre_scores_gemma":[0.9962515,0.0003880595,0.000432994,0.0001193419,0.0003259242,0.000004711893,0.000002667114,0.00002996883,0.002444819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8365452,"threshold_uncertainty_score":0.4096541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07666035497076303,"score_gpt":0.2555874198756452,"score_spread":0.1789270649048822,"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."}}