{"id":"W7128642374","doi":"10.1109/ictmod66732.2025.11371821","title":"AI Against Smishing in Kenya: Culturally Adapted SMS Scam Detection for Digital Trust","year":2025,"lang":"","type":"article","venue":"","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski; Optech (Canada)","funders":"","keywords":"Recall; Inclusion (mineral); Precision and recall; Logistic regression; Mobile device; Financial inclusion","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":[0.001046093,0.0005828014,0.0003091269,0.001240416,0.0009097583,0.0009991198,0.0004408919,0.0006266712,0.00117582],"category_scores_gemma":[0.004337332,0.0001802511,0.0002895617,0.0005773352,0.0003379404,0.001134493,0.000930133,0.0007656979,0.0006814266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001162904,"about_ca_system_score_gemma":0.001173445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02844908,"about_ca_topic_score_gemma":0.05567576,"domain_scores_codex":[0.9994251,0.0002836413,0.00003590022,0.0000843794,0.00007231201,0.00009872594],"domain_scores_gemma":[0.9986503,0.0005316605,0.000264885,0.0001350184,0.0002671996,0.000151096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006849717,0.0012758,0.7287028,0.0003811591,0.0001360228,0.001259228,0.004009627,0.01061144,0.009809489,0.002036277,0.009522193,0.2315709],"study_design_scores_gemma":[0.0000930566,0.0007163283,0.4536293,0.0004522762,0.0001921256,0.0009210745,0.02004035,0.48893,0.01363554,0.004349786,0.01687146,0.0001686956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987918,0.0002483758,0.005562653,0.001038847,0.00004028729,0.0002054958,0.0007509087,0.0001971658,0.004038347],"genre_scores_gemma":[0.9897465,0.0001185559,0.008438725,0.0001178204,0.0000112198,0.00005863941,0.0005996549,0.000009495933,0.0008994724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02844908,"threshold_uncertainty_score":0.05656701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01143852765360824,"score_gpt":0.244021652796965,"score_spread":0.2325831251433567,"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."}}