{"id":"W2898954836","doi":"10.1007/s11156-018-0768-8","title":"Asymmetric sensitivity of executive bonus compensation to earnings and the effect of regulatory changes","year":2018,"lang":"en","type":"article","venue":"Review of Quantitative Finance and Accounting","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Canadian Academic Accounting Association","keywords":"Executive compensation; Earnings; Accounting; Corporate finance; Conservatism; Business; Compensation (psychology); Incentive; Audit; Economics; Finance; Microeconomics; Political science; Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00407458,0.0001900229,0.0007293061,0.0002010157,0.0001454794,0.00002972454,0.0001241972,0.00003284481,0.000006185989],"category_scores_gemma":[0.01283799,0.0001383623,0.0000723292,0.001013148,0.0005016411,0.0004650159,0.0002763654,0.0001079288,0.000008552509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001058928,"about_ca_system_score_gemma":0.000008517071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003768518,"about_ca_topic_score_gemma":0.00003154634,"domain_scores_codex":[0.9986557,0.0001179381,0.0004360601,0.0002841262,0.000319936,0.0001862772],"domain_scores_gemma":[0.9889575,0.0008084045,0.009548644,0.0001923675,0.000486938,0.000006101939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005646149,0.00005114076,0.1438842,0.05071691,0.0002019083,0.000005206546,0.001079632,0.00002041563,0.00462813,0.1285593,0.00276928,0.6675193],"study_design_scores_gemma":[0.002389876,0.0005319335,0.8504034,0.04011166,0.0005787797,0.000004722436,0.0004243428,0.002479545,0.007206994,0.0003465892,0.09491178,0.0006103282],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9726428,0.01682121,0.006777563,0.0006625882,0.00008159822,0.0008785442,0.000005535164,0.00001642839,0.002113783],"genre_scores_gemma":[0.9932458,0.005518571,0.0004330191,0.0005999623,0.0001260941,0.00001709856,0.000002990057,0.00001616726,0.0000403353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7065192,"threshold_uncertainty_score":0.9954773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01207730392678929,"score_gpt":0.258152721012306,"score_spread":0.2460754170855167,"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."}}