Aaron Hanlon

We argue that textual large language model (LLM) outputs form an emergent genre, which we call stochascript. Following Ralph Cohen’s “empirical-historical” theory, we treat genres not as fixed sets of traits but as evolving categories shaped by social and technological change. LLM outputs resist placement as fiction, nonfiction, or bullshit: they lack fictive intent, do not always invite make-believe, are not reliably informational, and remain indifferent to truth while optimized to seem helpful. Their convergence on relevance and verisimilitude, and our patterns of interactions with LLM outputs, suggest a new genre is emerging. Naming stochascript as a genre clarifies its functions and cultural significance.

This article questions the extent to which interpretation explains literature, arguing that interpretation and explanation are not the same thing. It first engages with recent critical discussions of method and explanation in literary studies, finding that they are not much about method at all. It then offers a methodological framework that goes beyond various "method wars" over "critique" and "postcritique," and toward ways of explaining literature that are not reducible to matters of interpretation.