{"id":"W2462409249","doi":"10.1002/cjas.1383","title":"Geography and capital structure","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration","topic":"Corporate Finance and Governance","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Wilfrid Laurier University","funders":"","keywords":"Leverage (statistics); Capital structure; Capital (architecture); Information asymmetry; Economic geography; Constraint (computer-aided design); Location; Economics; Business; Microeconomics; Geography; Finance; Computer science; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0003093574,0.00013376,0.0001911994,0.000994412,0.0003071197,0.00143052,0.0001724789,0.0003245488,0.00549453],"category_scores_gemma":[0.004667625,0.00008317443,0.0001283674,0.001497634,0.001093773,0.0008589333,0.0007826612,0.0002420814,0.0003645242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008981957,"about_ca_system_score_gemma":0.0003701492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005195225,"about_ca_topic_score_gemma":0.006736067,"domain_scores_codex":[0.9996679,0.0001229125,0.0000167116,0.00005372618,0.00007479414,0.00006397288],"domain_scores_gemma":[0.9922651,0.002414902,0.004034677,0.0002719059,0.0003054048,0.0007080866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001299071,0.00008409021,0.9290008,0.00008188024,0.0001966252,0.0005519722,0.0008764081,0.01047853,0.00120198,0.03159628,0.001012868,0.02478867],"study_design_scores_gemma":[0.00002920547,0.00013334,0.9603084,0.00005753654,0.00008489866,0.0002982454,0.00100038,0.003640358,0.0007096468,0.02787254,0.005843749,0.00002160202],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9837798,0.0007527845,0.001002609,0.000538808,0.000005068225,0.00001188066,0.0002237069,0.000008194002,0.01367711],"genre_scores_gemma":[0.9994287,0.000103293,0.00009636135,0.00001060534,0.000004854712,0.000001235267,0.00002702891,6.695316e-7,0.0003271841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00549453,"threshold_uncertainty_score":0.01838106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04599837299819517,"score_gpt":0.2557893382499696,"score_spread":0.2097909652517744,"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."}}