{"id":"W3121757439","doi":"","title":"Earnings Conference Calls and Institutional Monitoring: Evidence from Textual Analysis","year":2019,"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":true,"ca_institutions":"Government of Canada","funders":"","keywords":"Institutional investor; Earnings; Business; Tone (literature); Accounting; Monetary economics; Economics; Finance; Corporate governance; Linguistics","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":[0.005704944,0.0001584945,0.0001756725,0.00454878,0.0004158322,0.002617625,0.000417539,0.000488669,0.004867221],"category_scores_gemma":[0.08751193,0.0001182117,0.0001344242,0.006208666,0.0007514005,0.002094064,0.001196194,0.0005461856,0.0007925123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007075951,"about_ca_system_score_gemma":0.0005436865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002624994,"about_ca_topic_score_gemma":0.004271821,"domain_scores_codex":[0.9945299,0.002149992,0.0007542889,0.000432322,0.0018019,0.0003315728],"domain_scores_gemma":[0.6817034,0.1692403,0.1298229,0.007249638,0.009911193,0.002072742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006437568,0.0001841752,0.9063376,0.0007247702,0.0001084971,0.0004102433,0.01187304,0.0004144277,0.001708609,0.00176698,0.005290064,0.07053775],"study_design_scores_gemma":[0.00001666295,0.00008858084,0.9807636,0.0002678047,0.0000574848,0.0001830196,0.006093913,0.001123604,0.001280169,0.00079244,0.009294407,0.00003837869],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981924,0.001226294,0.0007383,0.000810193,0.00004215906,0.0000426206,0.003875635,0.00003527897,0.01130564],"genre_scores_gemma":[0.9963565,0.0005019652,0.0003905568,0.00009953552,0.0001176736,0.00002625028,0.001626538,0.00001552717,0.000865528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005704944,"threshold_uncertainty_score":0.03017098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01192785924625517,"score_gpt":0.2238904075641691,"score_spread":0.2119625483179139,"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."}}