{"id":"W1981102846","doi":"10.1108/02686901111124639","title":"Client‐specific litigation risk and audit quality differentiation","year":2011,"lang":"en","type":"article","venue":"Managerial Auditing Journal","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Windsor","funders":"","keywords":"Audit; Litigation risk analysis; Quality audit; Business; Accounting; Big data; Quality (philosophy); Big Four; Extant taxon; Joint audit; Originality; Actuarial science; Internal audit; Psychology; 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":[0.008737236,0.0002101498,0.000663347,0.001584715,0.001105384,0.0033733,0.0008093169,0.0009288887,0.005829115],"category_scores_gemma":[0.0755457,0.0002341713,0.000624826,0.002085443,0.0022294,0.001547234,0.003270031,0.001524283,0.0004645078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002931384,"about_ca_system_score_gemma":0.001911432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003673769,"about_ca_topic_score_gemma":0.004235972,"domain_scores_codex":[0.9828101,0.00555903,0.002479453,0.001470555,0.005949767,0.001731068],"domain_scores_gemma":[0.8046571,0.07336302,0.09697403,0.009407313,0.007299972,0.008298559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003036999,0.0002545608,0.9712965,0.00004659903,0.0001151319,0.0002400859,0.001329222,0.001124023,0.0004983669,0.001930721,0.0002453353,0.02261579],"study_design_scores_gemma":[0.00003031972,0.0002577378,0.9878429,0.00005794117,0.00005592853,0.001191523,0.002324734,0.002421963,0.001047238,0.004043839,0.0006914624,0.0000344707],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928591,0.0004140547,0.001663171,0.0005129851,0.000005923994,0.00005328083,0.00006113164,0.00001817109,0.004412199],"genre_scores_gemma":[0.9994513,0.00004542931,0.0001814876,0.00003991296,0.000004190422,0.000006517706,0.00001985218,0.00000209518,0.0002491569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008737236,"threshold_uncertainty_score":0.04620749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02259168308705264,"score_gpt":0.2184055840886134,"score_spread":0.1958139010015608,"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."}}