  This paper introduces a multi-level, multi-label text classification dataset
comprising over 3000 documents. The dataset features literary and critical
texts from 19th-century Ottoman Turkish and Russian. It is the first study to
apply large language models (LLMs) to this dataset, sourced from prominent
literary periodicals of the era. The texts have been meticulously organized and
labeled. This was done according to a taxonomic framework that takes into
account both their structural and semantic attributes. Articles are categorized
and tagged with bibliometric metadata by human experts. We present baseline
classification results using a classical bag-of-words (BoW) naive Bayes model
and three modern LLMs: multilingual BERT, Falcon, and Llama-v2. We found that
in certain cases, Bag of Words (BoW) outperforms Large Language Models (LLMs),
emphasizing the need for additional research, especially in low-resource
language settings. This dataset is expected to be a valuable resource for
researchers in natural language processing and machine learning, especially for
historical and low-resource languages. The dataset is publicly available^1.
