{"id":"W4413778888","doi":"10.2139/ssrn.5406084","title":"Who Benefits More from Enhanced Information Transparency Under IFRS 16? — Asymmetric Capital Market Effects","year":2025,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Transparency (behavior); Business; Information asymmetry; Capital market; Accounting; Monetary economics; Capital (architecture); Industrial organization; Economics; Finance; 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":[0.009132465,0.0003054814,0.0008067075,0.0008340655,0.0006610161,0.00387605,0.0007823604,0.002208133,0.02589121],"category_scores_gemma":[0.04109248,0.0001889269,0.0005628609,0.0006439056,0.001626252,0.004187894,0.001493612,0.001594705,0.002796103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001064841,"about_ca_system_score_gemma":0.002429043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002340679,"about_ca_topic_score_gemma":0.001759096,"domain_scores_codex":[0.9959666,0.001270618,0.0002129453,0.0004295976,0.001025345,0.001094926],"domain_scores_gemma":[0.9452188,0.02313135,0.01679767,0.007946418,0.003608804,0.003296997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.007770879,0.001814647,0.08743889,0.0009136511,0.0006007009,0.001107829,0.001510828,0.01567483,0.01295128,0.3391092,0.0644049,0.4667023],"study_design_scores_gemma":[0.001807505,0.003328559,0.1769094,0.000533225,0.001028802,0.001321416,0.002924033,0.0422563,0.02012055,0.6609522,0.08855078,0.0002671834],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7885081,0.001881623,0.0128082,0.0467577,0.0007281859,0.0001405359,0.001795,0.001013921,0.1463668],"genre_scores_gemma":[0.9936885,0.0001636572,0.001142603,0.001159649,0.000414435,0.00001541051,0.00009684522,0.00003788349,0.003281075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02589121,"threshold_uncertainty_score":0.08661473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002785660205605037,"score_gpt":0.1895400378319193,"score_spread":0.1867543776263143,"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."}}