{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005837094,0.0004262421,0.0004142647,0.0005324659,0.0005749478,0.003233844,0.0009158463,0.0003824967,0.00001624227],"category_scores_gemma":[0.0004654606,0.0004170557,0.0002883033,0.002074268,0.00007644221,0.003898583,0.0003109288,0.0005627126,0.00002646602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004578696,"about_ca_system_score_gemma":0.0002279411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002650713,"about_ca_topic_score_gemma":0.001024904,"domain_scores_codex":[0.9969568,0.00008146613,0.000803683,0.001079049,0.0003668277,0.0007121987],"domain_scores_gemma":[0.9985014,0.0002361179,0.0001897687,0.0005834032,0.0003443222,0.0001449925],"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":[0.0002131606,0.0002028974,0.001197432,0.0001306335,0.00007652181,0.000007911847,0.001293658,0.003662876,0.00478092,0.003076962,0.00141135,0.9839457],"study_design_scores_gemma":[0.002159465,0.0003821384,0.005157175,0.0004346371,0.00003117584,0.00001070408,0.0003878186,0.9544691,0.01761994,0.003815954,0.01486782,0.0006640831],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09592572,0.0003037976,0.8821338,0.003174434,0.005037092,0.001059048,0.00001227888,0.0003914345,0.01196245],"genre_scores_gemma":[0.9921139,0.00004757308,0.001782458,0.001623359,0.0002793314,0.00007292343,0.00001187181,0.00002074128,0.004047814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9832816,"threshold_uncertainty_score":0.9998281,"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."}}