{"id":"W2778776359","doi":"10.5430/ijfr.v9n1p132","title":"Multi-Criteria Decision-Making Model Evaluating the Performance of Vietnamese Commercial Banks","year":2017,"lang":"en","type":"article","venue":"International Journal of Financial Research","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vietnamese; Analytic hierarchy process; Multiple-criteria decision analysis; Computer science; Rank (graph theory); TOPSIS; Fuzzy logic; Ideal solution; Process (computing); Decision-making models; Operations research; Hierarchy; Preference; Order (exchange); Artificial intelligence; Engineering; Business; Mathematics; Statistics; Economics; Finance","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":["metaresearch","scholarly_communication","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03761687,0.0002031977,0.0005506678,0.001137509,0.001256325,0.00162764,0.00967531,0.0001553268,0.000482492],"category_scores_gemma":[0.1369921,0.0001291502,0.0003867963,0.0003917074,0.0007015821,0.001496014,0.002026547,0.001170818,0.00008599574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002275762,"about_ca_system_score_gemma":0.001299211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003202276,"about_ca_topic_score_gemma":0.00007826501,"domain_scores_codex":[0.985476,0.0007559601,0.002280683,0.0004206639,0.01057159,0.0004950677],"domain_scores_gemma":[0.9767438,0.006918658,0.002180012,0.001307909,0.0127002,0.0001493789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001739969,0.0001524147,0.009465232,0.000004130326,0.00002627951,0.00006452617,0.001309525,0.00439742,0.008776143,0.0004026227,0.002951303,0.9707105],"study_design_scores_gemma":[0.00135502,0.0003392177,0.3730729,0.0006793501,0.000008462408,0.00008157659,0.00008432759,0.6150051,0.000640532,0.0074283,0.001162252,0.0001429669],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9717697,0.0001447446,0.02282211,0.00158836,0.002848556,0.0002135838,0.00003657628,0.000004185442,0.000572197],"genre_scores_gemma":[0.957509,0.00006243715,0.04112116,0.0001412851,0.0008704477,0.000006715828,4.482935e-7,0.00002059297,0.0002679664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9705675,"threshold_uncertainty_score":0.9994088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5291383708662041,"score_gpt":0.6335163545237871,"score_spread":0.104377983657583,"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."}}