Author attribution is a core task in stylometry and computational text analysis, with applications in digital humanities, literary studies, and forensic linguistics. While extensively studied for high-resource languages, it remains largely unexplored for low-resource languages, including Uzbek. We introduce a new benchmark dataset for closed-set author attribution in Uzbek literary prose, comprising 54 authors and 269 works. To prevent information leakage, we adopt a work-level train–test split, ensuring that all text segments from a given literary work appear exclusively in either the training or test set. We benchmark classical stylometric approaches based on function words and character n-grams against four neural baselines, including three multilingual transformer encoders (mBERT, XLM-R, and mmBERT) and a character-level convolutional network. We also conduct an ablation study identifying (6–8) as the optimal character n-gram configuration, and analyze the effect of text length by evaluating all models across chunk sizes ranging from 100 to 800 words. In the course of this analysis we identify and correct an evaluation-unit mismatch under which classical models were scored per text chunk while neural models were scored after aggregating predictions across each work (per document): under this matched evaluation, classical character n-gram representations outperform every neural baseline, including mmBERT, at every chunk size and under both evaluation units, an advantage that a paired bootstrap significance test confirms is statistically robust (p<.05) across most of the chunk-size range, though not statistically distinguishable from zero at the largest chunk sizes (700–800 words) where the two models converge. This reverses the paper’s original, mixed-unit headline comparison (mmBERT 0.91 vs. n-grams 0.87 Macro-F1 at 800 words) and demonstrates that classical stylometric features remain a strong, computationally inexpensive foundation for low-resource author attribution. Beyond the Uzbek benchmark itself, we surface this evaluation-unit confound as a concrete, quantified cautionary case for classical-versus-neural comparisons in authorship attribution more broadly. We publicly release the dataset splits, labels, derived stylometric features, and evaluation scripts as a foundation for future research on Uzbek and other low-resource languages.