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When Victorian Becomes a Prompt: Literary Periodization as a Generative Constraint in 100 AI-Generated Novels

Sep 2026 · 0 citations · 25 references
Computer Science

TL;DR

Generative AI inverts the typical periodization of literary history: the periodizing tag Victorian can now come first and influence what is written, but not robustly in Llama workflows.

Abstract

Generative AI inverts the typical periodization of literary history: the periodizing tag Victorian can now come first and influence what is written. Generative periodization, defined and tested here, describes the use of literary-period designations in generating texts. I test this approach on 100 book-length novels produced under Victorian and Zero-Style conditions using GPT, Qwen, and Llama workflows. The Period Alignment Score (PAS), trained on nineteenth-century literature and benchmarked against human Zero-Style prose, assesses alignment using topic-reduced grammatical features. Victorian prompts produce consistent historical-direction shifts in GPT and Qwen, but not robustly in Llama. Victorian-only recalibration and harder comparison corpora preserve the GPT and Qwen effects. Cross-model transfer also shows a shared direction of grammatical change. The measurable target is the broader nineteenth century rather than the Victorian period per se.

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