The study proposes a large-scale stylometric comparison between human-authored English novels and machine-generated texts produced from schematic summaries of those novels. Its main hypothesis is that both human authors and large language models (LLMs) exhibit distinct, identifiable stylistic profiles, and that these profiles show systematic patterns of similarity and divergence. The research also examines whether author gender influences stylometric clustering.

Using a corpus of about 1,000 nineteenth- and twentieth-century novels, the study generates abstracts and then produces new texts from these using four different LLMs. Stylometric analysis is conducted with TF-IDF features, k-nearest neighbors, UMAP visualization, and Burrows’s Delta to compare human and machine texts across dimensions of authorship, origin (human vs. machine), and gender. The design controls for narrative content in order to isolate stylistic differences.

The expected outcome is evidence that LLMs develop their own recognizable stylistic tendencies, which may align with or diverge from human authors’ styles and potentially form independent stylistic clusters. The study aims to shift stylometry beyond plagiarism detection toward understanding machines as possible stylistic agents, contributing to debates in digital humanities and NLP about authorship, stylistic identity, and the nature of style in artificial language production.