{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001784944,0.0005900696,0.0007315833,0.0006794934,0.0008194313,0.0008309355,0.0007640963,0.0001581969,0.00009671434],"category_scores_gemma":[0.00204881,0.0006120634,0.0001926304,0.0009128159,0.0002599471,0.002907374,0.0006462119,0.0007685139,0.00008416511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002827604,"about_ca_system_score_gemma":0.00006721709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007020645,"about_ca_topic_score_gemma":0.00005393156,"domain_scores_codex":[0.9957939,0.00003921921,0.001456355,0.0006451384,0.00120559,0.0008597987],"domain_scores_gemma":[0.9708385,0.0002378545,0.0278279,0.0003950901,0.0006654279,0.00003519235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001460148,0.00124344,0.1976953,0.004898581,0.00127621,0.003321664,0.0009761624,0.01654739,0.00191064,0.3176883,0.2842281,0.168754],"study_design_scores_gemma":[0.004454572,0.0001127038,0.3345641,0.002110392,0.000362625,0.00005408957,0.001110148,0.001019322,0.0002142783,0.003407921,0.6514702,0.00111958],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9596798,0.0003093313,0.01165901,0.001961784,0.001367976,0.0004539642,0.000008947364,0.0002110145,0.02434818],"genre_scores_gemma":[0.9882292,0.0003454311,0.004360858,0.001478739,0.002852605,0.00002423762,0.0000397561,0.0001140237,0.00255516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3672421,"threshold_uncertainty_score":0.9996331,"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."}}