{"id":"W4394862587","doi":"10.2139/ssrn.4787505","title":"Geographical Proximity, Cultural Familiarity and Financial Information Production","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Economic Growth and Development","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Production (economics); Business; Finance; 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":[0.0009709118,0.000192612,0.0002405899,0.001602886,0.000716486,0.003576767,0.0003301543,0.0008790536,0.01718362],"category_scores_gemma":[0.01636251,0.0002065497,0.000256705,0.002366686,0.001421986,0.002451121,0.001478745,0.0006135216,0.001119892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005793712,"about_ca_system_score_gemma":0.0004913496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007389298,"about_ca_topic_score_gemma":0.007239663,"domain_scores_codex":[0.9991872,0.000398593,0.00007154378,0.0001105841,0.0001325949,0.00009952915],"domain_scores_gemma":[0.9629289,0.02384651,0.008017163,0.001068446,0.001164399,0.002974554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006095385,0.000641095,0.9598351,0.0001052801,0.0001566172,0.001031583,0.005050082,0.001279923,0.00136766,0.005015615,0.0005122718,0.02439529],"study_design_scores_gemma":[0.00004925848,0.0003711022,0.9798473,0.00004680475,0.0001101518,0.0005715596,0.01001242,0.00147094,0.0003392352,0.00571943,0.001429915,0.0000317666],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869286,0.0003429527,0.0003755519,0.0002004919,0.000005724443,0.000007845016,0.00007615954,0.000005184017,0.01205742],"genre_scores_gemma":[0.9989728,0.0001036658,0.0001019327,0.00001451228,0.00001070962,0.000002386861,0.00003322963,0.000002531449,0.0007581866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01718362,"threshold_uncertainty_score":0.05748498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004934883197127574,"score_gpt":0.201016532137241,"score_spread":0.1960816489401134,"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."}}