{"id":"W3121412720","doi":"10.1016/j.aos.2018.02.004","title":"Management deception, big-bath accounting, and information asymmetry: Evidence from linguistic analysis","year":2018,"lang":"en","type":"article","venue":"Accounting Organizations and Society","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":100,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Deception; Accounting; Information asymmetry; Matching (statistics); Big data; Earnings; Business; Measure (data warehouse); Propensity score matching; Earnings management; Psychology; Social psychology; Computer science; Finance; Data mining","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":[0.01390841,0.0003460135,0.0005895436,0.003187616,0.002425944,0.005301239,0.001036717,0.001770904,0.006209911],"category_scores_gemma":[0.1207174,0.0003511106,0.0002732896,0.004051559,0.004488604,0.007173957,0.002920857,0.00214879,0.0005697678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001038549,"about_ca_system_score_gemma":0.001208287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005140721,"about_ca_topic_score_gemma":0.003580668,"domain_scores_codex":[0.9847634,0.008692556,0.001564316,0.0008835821,0.003541907,0.0005543212],"domain_scores_gemma":[0.7648477,0.1732403,0.0366712,0.01135032,0.01285745,0.001033044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004324536,0.001617435,0.4151876,0.002294314,0.0009040461,0.003651973,0.2151256,0.003042546,0.008714356,0.1489984,0.01124384,0.1848953],"study_design_scores_gemma":[0.0004321478,0.0004737827,0.5422115,0.001930307,0.0009308808,0.003640027,0.117298,0.02546289,0.005539767,0.2720158,0.02953051,0.0005343871],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9687735,0.001251332,0.004062819,0.003792422,0.00004468637,0.00002975548,0.0002708052,0.00001919559,0.02175535],"genre_scores_gemma":[0.998163,0.0002775926,0.0007084997,0.0002803649,0.00003370369,0.00001361534,0.0001452764,0.00001138831,0.0003666397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01390841,"threshold_uncertainty_score":0.07355553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00558063401143234,"score_gpt":0.2071288317590659,"score_spread":0.2015481977476335,"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."}}