{"id":"W4413131763","doi":"10.1111/1911-3846.13070","title":"The informational content of key audit matters: Evidence from using artificial intelligence in textual analysis","year":2025,"lang":"en","type":"article","venue":"Contemporary Accounting Research","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Katholische Universität Eichstätt-Ingolstadt; San Diego State University","keywords":"Goodwill; Predictive power; Audit; Key (lock); Artificial intelligence; Business; Comprehension; Empirical research; Content (measure theory); Computer science; Psychology; Machine learning; Accounting; Computer security; Epistemology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008301748,0.0003983716,0.0002550828,0.002993047,0.0004681163,0.002309058,0.0006291604,0.0008104027,0.002518856],"category_scores_gemma":[0.1422492,0.0002420052,0.000384319,0.002645401,0.001091117,0.004285645,0.001155662,0.001012949,0.0005681317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000541164,"about_ca_system_score_gemma":0.0005434743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002242143,"about_ca_topic_score_gemma":0.002057317,"domain_scores_codex":[0.9944945,0.003732467,0.0004207171,0.0004332978,0.0008153635,0.0001035508],"domain_scores_gemma":[0.6795626,0.2817393,0.02423816,0.006905169,0.006626648,0.0009280646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002483307,0.001052803,0.6666024,0.001642553,0.0005849623,0.000653994,0.004624576,0.01833869,0.005052761,0.006114556,0.004674865,0.2881746],"study_design_scores_gemma":[0.0001330253,0.0006406716,0.6483459,0.001109352,0.0006493083,0.0009072927,0.003301991,0.2823519,0.009171943,0.04274046,0.01044057,0.0002075393],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9729946,0.001472507,0.01419856,0.001818186,0.00005236502,0.00009224416,0.001461575,0.0002056475,0.007704258],"genre_scores_gemma":[0.9949533,0.0003602902,0.003501899,0.00010661,0.00006508177,0.00001862585,0.0007442434,0.00001780487,0.0002319795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008301748,"threshold_uncertainty_score":0.04390436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1270827251664847,"score_gpt":0.3387087952085664,"score_spread":0.2116260700420817,"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."}}