{"id":"W2964513510","doi":"10.1111/abac.12165","title":"Audit Adjustments and Public Sector Audit Quality","year":2019,"lang":"en","type":"article","venue":"Abacus","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Economic and Social Research Council; Queen's University","keywords":"Joint audit; Accounting; Audit; Business; Chief audit executive; Quality audit; Audit plan; Performance audit; Earnings management; Audit evidence; Public sector; Information technology audit; External auditor; Context (archaeology); Austerity; Earnings; Internal audit; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004153917,0.0002470446,0.0002767781,0.000146011,0.0001577803,0.0003990807,0.0003363145,0.00007274671,0.002474575],"category_scores_gemma":[0.001756041,0.0002399687,0.00007248131,0.0003795969,0.00005605372,0.001484463,0.0005414198,0.0001955134,0.0054767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006606081,"about_ca_system_score_gemma":0.00002003107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008277314,"about_ca_topic_score_gemma":0.0001405419,"domain_scores_codex":[0.9981898,0.00001812869,0.0003081448,0.000535183,0.0004534897,0.0004952452],"domain_scores_gemma":[0.9945678,0.0000542134,0.004825526,0.0004189755,0.0001064115,0.00002706527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003063766,0.0001296657,0.8021765,0.0005046928,0.0001363741,0.00001987824,0.00006411049,0.00002116956,0.0007000642,0.06037394,0.04388127,0.09196164],"study_design_scores_gemma":[0.0007650708,0.00001135466,0.4371614,0.00003864606,0.0000291171,0.000001005139,0.0001242993,0.0002345719,0.00001409659,0.0009355392,0.5603668,0.0003180623],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9164053,0.0001483286,0.002213793,0.001597137,0.0007590785,0.000423906,0.000006596931,0.0002586371,0.07818728],"genre_scores_gemma":[0.9874016,0.00002672221,0.00009984317,0.002857207,0.0008530719,0.00002316261,0.00002997912,0.00004475585,0.008663625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5164856,"threshold_uncertainty_score":0.9984373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01792357654784648,"score_gpt":0.2305644832132971,"score_spread":0.2126409066654506,"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."}}