{"id":"W7115743480","doi":"10.7910/dvn/drxi9f","title":"IFRS 18 AND THE GLOBAL STANDARDIZATION OF RESULT SUBTOTALS: A MULTI-JURISDICTIONAL ECONOMETRIC ANALYSIS IN THE LIGHT OF INSTITUTIONAL THEORY","year":2025,"lang":"","type":"dataset","venue":"Harvard Dataverse","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Standardization; Panel data; Institutional theory; International Financial Reporting Standards; Panel analysis; Sample (material); Random effects model; Econometric analysis; Multilevel model","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01824616,0.0003558675,0.0006063614,0.003920339,0.0004512205,0.001918166,0.001367306,0.0005436043,0.005593624],"category_scores_gemma":[0.07252156,0.0002514055,0.001425936,0.008579209,0.001143653,0.00132437,0.003038926,0.001154644,0.0008663355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001774853,"about_ca_system_score_gemma":0.001906151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04107103,"about_ca_topic_score_gemma":0.02604144,"domain_scores_codex":[0.9845276,0.008585407,0.001430268,0.001848023,0.002643713,0.0009649367],"domain_scores_gemma":[0.8940169,0.05976593,0.02851302,0.008838671,0.008126538,0.0007388979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004102067,0.0001746872,0.915371,0.0006736178,0.0008621806,0.0002220357,0.00212764,0.009429411,0.0003088492,0.01279778,0.02086211,0.03676041],"study_design_scores_gemma":[0.00009743797,0.0002302187,0.9209033,0.0002777823,0.0005312543,0.0001324661,0.004200922,0.01336819,0.001209289,0.00433385,0.05464021,0.00007504797],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.8828762,0.001119965,0.01644347,0.002106787,0.00008062793,0.0004654689,0.08396121,0.0003257373,0.0126206],"genre_scores_gemma":[0.938446,0.0002142563,0.008010679,0.0002098821,0.00004638445,0.0008246388,0.04998738,0.0000721977,0.002188573],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.04107103,"threshold_uncertainty_score":0.09649605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01455926846675186,"score_gpt":0.2751708227530297,"score_spread":0.2606115542862779,"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."}}