{"id":"W2584966560","doi":"10.1177/0148558x16688115","title":"Who Benefits From IFRS Convergence in China?","year":2017,"lang":"en","type":"article","venue":"Journal of Accounting Auditing & Finance","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"International Financial Reporting Standards; Convergence (economics); Capital market; Enforcement; Business; China; Stock market; Accounting; Monetary economics; Economics; Finance; Macroeconomics","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":[0.00158771,0.0001332778,0.0002459508,0.0008406967,0.0006631485,0.0009607514,0.0002916254,0.0005781298,0.003754964],"category_scores_gemma":[0.003372391,0.0001269787,0.0002575743,0.0006213599,0.000660455,0.0009539666,0.000946364,0.0005432175,0.0002954376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009492704,"about_ca_system_score_gemma":0.001961563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02129621,"about_ca_topic_score_gemma":0.02605831,"domain_scores_codex":[0.9994087,0.0001116014,0.00003830238,0.00006905398,0.00009970543,0.0002726714],"domain_scores_gemma":[0.997352,0.0002354734,0.001316955,0.0001212481,0.0002847644,0.0006895208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001334285,0.0001372249,0.95528,0.00005343722,0.00004655187,0.0008312626,0.003011336,0.0002352523,0.0006374781,0.001421736,0.001842479,0.0363699],"study_design_scores_gemma":[0.00001007141,0.0001172394,0.9929844,0.00002682985,0.00002111228,0.0001931399,0.002861009,0.0005427182,0.0002449563,0.0005584919,0.002428651,0.00001136323],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953725,0.000245332,0.00003667026,0.001837328,0.00001893017,0.00001190002,0.00006875392,0.000005508137,0.002403056],"genre_scores_gemma":[0.9988731,0.0001731493,0.00002491043,0.0002049519,0.00002180797,0.000003393084,0.00005675774,0.000001419491,0.000640471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02129621,"threshold_uncertainty_score":0.04234451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01023578601573699,"score_gpt":0.2189493028885109,"score_spread":0.2087135168727739,"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."}}