{"id":"W3124604040","doi":"","title":"Information Externalities and Intermediaries in Frictional Search Markets","year":2010,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Intermediary; Externality; Search cost; Matching (statistics); Business; Microeconomics; Transaction cost; Space (punctuation); Value (mathematics); Industrial organization; Commerce; Economics; Marketing; 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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.006165403,0.0002536282,0.0006196422,0.001434628,0.0001208337,0.0004213124,0.0005166485,0.0005852423,0.0002364351],"category_scores_gemma":[0.0007956671,0.0003193703,0.00009618059,0.0001183172,0.0004496619,0.0004750359,0.0008845097,0.002486333,0.00006937575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000477952,"about_ca_system_score_gemma":0.0001562439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004462473,"about_ca_topic_score_gemma":0.0003509899,"domain_scores_codex":[0.997385,0.0001793141,0.001222441,0.0005056311,0.00008631904,0.0006213562],"domain_scores_gemma":[0.9983519,0.0005902017,0.0003115203,0.0005545751,0.00006077732,0.0001309768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004080349,0.0001968886,0.4767321,0.001312392,0.0001391721,0.00002152121,0.01626885,0.001745484,0.00001685759,0.4211819,0.00008271959,0.08189413],"study_design_scores_gemma":[0.002556273,0.0001623566,0.5718871,0.00107741,0.00000490318,0.00006503056,0.00847022,0.04752721,0.00009049781,0.2877645,0.07877921,0.001615269],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9151247,0.0002800096,0.00002695324,0.0002769673,0.00102803,0.0004817598,0.0002365011,0.00002452753,0.08252054],"genre_scores_gemma":[0.9949493,0.002950887,0.0001781409,0.00005023196,0.0002304128,0.0001670588,0.00007156748,0.00002927714,0.001373115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1334173,"threshold_uncertainty_score":0.9999259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03274584077152505,"score_gpt":0.2766165255339555,"score_spread":0.2438706847624305,"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."}}