{"id":"W4416187161","doi":"10.28924/2291-8639-23-2025-287","title":"Predicting Financial Distress in ASEAN Banking: A Logistic Regression Approach","year":2025,"lang":"","type":"article","venue":"International Journal of Analysis and Applications","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Logistic regression; Financial crisis; Financial ratio; Panel data; Loan; Warning system; Exchange rate; Sample (material); Credit risk","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":[0.0006556361,0.0002516174,0.0005479612,0.00203349,0.0002531316,0.0006071991,0.0006210614,0.0001621789,0.00008039697],"category_scores_gemma":[0.0002983076,0.0002240007,0.0003843047,0.00233875,0.0001463607,0.0007098616,0.0002635409,0.0004228608,0.000003217797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001359489,"about_ca_system_score_gemma":0.0001119752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005282372,"about_ca_topic_score_gemma":0.0002611328,"domain_scores_codex":[0.9975159,0.00003471952,0.001260862,0.00038815,0.0005694487,0.0002309369],"domain_scores_gemma":[0.9974653,0.0000961112,0.001259246,0.0002066797,0.0009422195,0.00003050055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003278672,0.001506857,0.752168,0.000301493,0.001586786,0.00004046794,0.000110714,0.0054072,0.0001409673,0.1032234,0.001097459,0.1340888],"study_design_scores_gemma":[0.001764944,0.00002692902,0.8528906,0.001047003,0.003509155,0.000009108727,0.0005494618,0.09923537,0.00002679444,0.01358991,0.02698451,0.0003661948],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4059095,0.006296625,0.5557894,0.004936148,0.001836569,0.0008563012,0.0002625976,0.00004877619,0.0240641],"genre_scores_gemma":[0.9968314,0.000733867,0.0002765713,0.0002595378,0.00149277,0.00003623045,0.0001615038,0.000009685054,0.0001984874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5909218,"threshold_uncertainty_score":0.9134485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01295709215412024,"score_gpt":0.2761991854755827,"score_spread":0.2632420933214625,"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."}}