{"id":"W4410900811","doi":"10.5267/j.dsl.2025.4.006","title":"Factors affecting the decisions of tax compliance selection of Vietnamese enterprises: A mega data analysis","year":2025,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Taxation and Compliance Studies","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mega-; Vietnamese; Selection (genetic algorithm); Compliance (psychology); Business; Computer science; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001511861,0.00011579,0.000428798,0.0009185006,0.0003994817,0.00008247959,0.00147973,0.00002553616,0.0000661771],"category_scores_gemma":[0.002429589,0.00008699491,0.0001528577,0.005826476,0.0004456629,0.0003993174,0.0004669071,0.0001050443,0.00001052693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006697561,"about_ca_system_score_gemma":0.00003232079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001281946,"about_ca_topic_score_gemma":0.00009926554,"domain_scores_codex":[0.9983386,0.00002232132,0.0007025923,0.0005522955,0.0001768113,0.0002073373],"domain_scores_gemma":[0.997484,0.0007475548,0.0006611034,0.0009607035,0.0001117952,0.000034828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002384165,0.00007995853,0.977677,0.0000105952,0.0002218831,2.588475e-7,0.001242777,0.002359265,0.002005511,0.00415711,0.003791144,0.008430662],"study_design_scores_gemma":[0.0002194517,0.00001908065,0.9791581,0.00008520463,0.00004798306,2.940868e-7,0.001196378,0.01349829,0.0008031178,0.002332756,0.002508619,0.0001307247],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6092545,0.0001875739,0.3884713,0.000893975,0.0002777579,0.0001178251,0.0000888619,0.00000998955,0.0006982614],"genre_scores_gemma":[0.996258,0.00004490075,0.003132283,0.0004906181,0.000008987875,0.000003988797,0.000004165023,0.000003468009,0.0000535622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3870036,"threshold_uncertainty_score":0.3547549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1342186796328468,"score_gpt":0.3413981557518381,"score_spread":0.2071794761189913,"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."}}