{"id":"W1845378171","doi":"10.1177/0148558x0602100203","title":"Executive Compensation, Investment Opportunities, and Earnings Management: High-Tech Firms versus Low-Tech Firms","year":2006,"lang":"en","type":"article","venue":"Journal of Accounting Auditing & Finance","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Accrual; Earnings; Business; Executive compensation; Earnings management; High tech; Cash; Stock (firearms); Investment (military); Compensation (psychology); Accounting; Finance; Monetary economics; Economics; Corporate governance","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":[0.001183325,0.0001871669,0.0002214681,0.001432535,0.0004448875,0.001618433,0.0003031956,0.0005513135,0.002339924],"category_scores_gemma":[0.004864074,0.00009745159,0.000277987,0.001210615,0.000502689,0.0005817724,0.001143752,0.0004281552,0.0003040345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003458633,"about_ca_system_score_gemma":0.0003082878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002098465,"about_ca_topic_score_gemma":0.005680898,"domain_scores_codex":[0.999184,0.0001228738,0.00007216506,0.0001099002,0.0002137306,0.0002973553],"domain_scores_gemma":[0.9799754,0.005204383,0.01124765,0.000446691,0.0005434306,0.002582444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008600391,0.00005572921,0.9966713,0.000008200328,0.00003444045,0.0000877878,0.0001351962,0.00006748221,0.0003583572,0.0001514096,0.00004959865,0.002294618],"study_design_scores_gemma":[0.000003452657,0.00003348919,0.9993051,0.000004274279,0.000008946336,0.00004589673,0.0002448154,0.00008960992,0.00008274446,0.00006564198,0.0001138966,0.000002052947],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990873,0.00009787898,0.0000385034,0.00004372383,0.000001804648,0.000002425378,0.00004695259,9.846912e-7,0.0006803463],"genre_scores_gemma":[0.9995673,0.0000338053,0.00003051524,0.00001889318,0.00001034416,0.000001277913,0.0001023982,5.083334e-7,0.0002349598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002339924,"threshold_uncertainty_score":0.007827818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01317951672912223,"score_gpt":0.2067077829728271,"score_spread":0.1935282662437049,"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."}}