{"id":"W4399145792","doi":"10.2139/ssrn.4846845","title":"Geographical Proximity, Cultural Familiarity and Financial Information Production","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Production (economics); Business; Finance; Economic geography; Geography; Economics","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":[0.001058385,0.000198859,0.0003257118,0.001191976,0.0004127434,0.003111859,0.0002347704,0.0008730532,0.01245374],"category_scores_gemma":[0.01632216,0.00021075,0.0002632258,0.002207577,0.001299296,0.001887509,0.001029508,0.0005406703,0.00082072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000411411,"about_ca_system_score_gemma":0.0002824366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003998271,"about_ca_topic_score_gemma":0.003300861,"domain_scores_codex":[0.999278,0.0003753623,0.00004900561,0.0001369217,0.000092127,0.00006863522],"domain_scores_gemma":[0.9678712,0.02399393,0.004922327,0.001149351,0.0007413041,0.001321863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009764608,0.0005423877,0.9078285,0.000242864,0.000483249,0.001276248,0.005101127,0.006420287,0.003650029,0.02024306,0.001185719,0.05205015],"study_design_scores_gemma":[0.00007233091,0.0003570988,0.9538673,0.00006544007,0.0002636976,0.0006256932,0.004962622,0.006492287,0.0007848711,0.03079088,0.001665195,0.00005264289],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879885,0.0006274548,0.002101579,0.0003671716,0.000008770267,0.000009890228,0.0001697101,0.00000910335,0.008717923],"genre_scores_gemma":[0.9987888,0.0001582769,0.0002602889,0.00001833651,0.0000156389,0.000003333406,0.00004626513,0.000003714097,0.0007053713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01245374,"threshold_uncertainty_score":0.04166192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.011243513639967,"score_gpt":0.222098979021111,"score_spread":0.210855465381144,"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."}}