当维多利亚时代成为提示：文学分期作为100部AI生成小说中的生成约束
When Victorian Becomes a Prompt: Literary Periodization as a Generative Constraint in 100 AI-Generated Novels
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中文总结 AI 辅助
本文提出“生成式分期”方法，利用文学分期标签作为提示约束，在100部AI生成小说中测试，发现维多利亚时代提示在GPT和Qwen中产生一致的历史方向偏移，但在Llama中不稳健。
中文摘要 AI 辅助
生成式AI颠覆了文学史中典型的分期方式：分期标签“维多利亚时代”如今可以先行出现，并影响所生成的内容。本文定义并测试了“生成式分期”，即利用文学分期指定来生成文本。我在GPT、Qwen和Llama工作流下，对100部在“维多利亚时代”和“零风格”条件下生成的长篇小说进行了测试。分期对齐分数（PAS）基于十九世纪文学训练，并以人类“零风格”散文为基准，通过主题缩减的语法特征评估对齐程度。维多利亚时代提示在GPT和Qwen中产生了一致的历史方向偏移，但在Llama中并不稳健。仅使用维多利亚时代的重新校准和更严格的比较语料库保留了GPT和Qwen的效果。跨模型迁移也显示出语法变化的共同方向。可测量的目标是更广泛的十九世纪，而非特指维多利亚时期。
英文摘要
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.
