{"id":"W2795173345","doi":"10.1111/iere.12458","title":"ADVERSE SELECTION WITH HETEROGENEOUSLY INFORMED AGENTS","year":2020,"lang":"en","type":"article","venue":"International Economic Review","topic":"Economic theories and models","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Adverse selection; Market liquidity; Asset (computer security); Welfare; Private information retrieval; Microeconomics; Information asymmetry; Quality (philosophy); Measure (data warehouse); Economics; Business; Monetary economics; Computer science","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001684656,0.0001603233,0.0004076736,0.00005691907,0.00004226697,0.00004069378,0.0003333348,0.00004265287,0.00476696],"category_scores_gemma":[0.00004730923,0.0001699777,0.0001507077,0.0000463331,0.00003150221,0.0004150732,0.00005963696,0.00009266957,0.004550262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002707898,"about_ca_system_score_gemma":0.00005088133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006192052,"about_ca_topic_score_gemma":0.00003259216,"domain_scores_codex":[0.9986955,0.000006829856,0.0007202944,0.0003796198,0.00002143102,0.0001763047],"domain_scores_gemma":[0.9992239,0.00002255499,0.0004533328,0.0001537726,0.00002214714,0.0001242673],"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":[0.0001916508,0.00008126767,0.03199055,0.0009498436,0.001082836,0.00001535896,0.0005820257,0.00916849,0.000006510889,0.8899849,0.05577081,0.01017576],"study_design_scores_gemma":[0.0005883763,0.00008455582,0.0005288994,0.0001700152,0.00001232471,0.00003079481,0.00001270407,0.007816312,0.00002238681,0.002792673,0.9876576,0.0002833431],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3062238,0.03079639,0.01213395,0.051546,0.003677044,0.002586356,0.001155549,0.0004118805,0.591469],"genre_scores_gemma":[0.945026,0.03297493,0.001239023,0.01827117,0.0005907629,0.0001087365,0.00012041,0.00005544713,0.00161355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9318868,"threshold_uncertainty_score":0.9962248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04646130602745427,"score_gpt":0.2567225897855662,"score_spread":0.2102612837581119,"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."}}