{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00401702,0.0009605867,0.001914729,0.0008632113,0.0008197813,0.003910599,0.002124059,0.003713085,0.01457656],"category_scores_gemma":[0.009709866,0.0005873361,0.001238209,0.0007499383,0.003036657,0.00268913,0.001630469,0.002543134,0.001505869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001394548,"about_ca_system_score_gemma":0.0009904659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003015344,"about_ca_topic_score_gemma":0.001294293,"domain_scores_codex":[0.9971143,0.001785005,0.00009095431,0.0003621375,0.0002827044,0.000364945],"domain_scores_gemma":[0.9896938,0.005900554,0.002269314,0.0006696158,0.0007172209,0.0007494126],"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":[0.0002339677,0.0001236349,0.003042801,0.0001044901,0.0001437468,0.0009327969,0.0002312889,0.1629522,0.0006526302,0.8182458,0.006067964,0.007268746],"study_design_scores_gemma":[0.0004071609,0.0001661632,0.001199311,0.00005042127,0.0000838704,0.0003714601,0.0001329369,0.4466145,0.0001706239,0.5454675,0.005265924,0.00007005443],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3428231,0.003393496,0.5139189,0.02491141,0.001280656,0.000422434,0.001376444,0.0003360221,0.1115376],"genre_scores_gemma":[0.9606001,0.001247329,0.006113205,0.0007708711,0.0004790836,0.0001817916,0.0001286546,0.00001576804,0.03046319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01457656,"threshold_uncertainty_score":0.04876345,"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."}}