Sani et al. (2025) introduced the first dedicated Hausa machine-generated text (MGT) detec- tor, an AfroXLMR-large classifier trained on 2,586 paired articles from seven Hausa news outlets and Gemini 2.0 Flash, reporting F1 of 0.9921 on a headline-grouped split. They iden- tified three limitations of their work: a single news domain, a small batch and short train- ing schedule, and a single generator (Gemini 2.0 Flash) for the machine-generated data. We extend their work along the third axis. First, we replicate their reported result across four random seeds, obtaining F1 of 0.992 ±0.005. Test recall on the AI class is 1.000 at every seed. Second, we analyse the detector’s false positives. All errors fall on Human articles, and two native-speaker reviewers confirm these are genuine, natural Hausa journalism. The clear- est correlate of misclassification is the absence of numerical content: the median digit frac- tion is 0.0% in misclassified articles vs. 0.6% in correctly classified ones. Third, we test the detector on six non-training generators span- ning four model families. Detection rates span from 0% (Aya-Expanse 8B) to nearly 100% (Gemma 4). All three Google-family models in our experiment (training-distribution Gemini 2.0 Flash and the two non-training Gemma 4 variants) are detected at rates above 98% with mean P(machine) above 0.98. No non-Google generator exceeds 21%. Three contamination checks fail to explain this away: generated articles overlap very little with their human sources, detection barely moves when we re- move every article that shares a 13-gram with its source, and the Google output that gets de- tected is at least as diverse as everyone else’s, so it is not memorised or templated text. What the detector has learned looks less like a general Hausa MGT signal and more like a Gemini- family stylistic fingerprint. We discuss what this means for deployment, including a 3.1% false-positive rate on real Hausa journalists.

We present an improved method for automaticparallel sentence alignment in low- resourcelanguages. We used CoHere multilingualembeddings and inverted softmax retrieval.Our technique achieved a higher F1-score of78.30% on the MAFAND-MT test set, comparedto the existing technique’s 54.75%. Precisionand recall have shown similar performance.We assessed the quality of the extracted data bydemonstrating that it outperforms the existingtechnique in terms of low-resource translationperformance.