Towards a Perspectival Moral History of the Novel Using LLMs
Bibliographic record
Abstract
This paper introduces a new framework for studying the moral history of the novel through the lens of large language models (LLMs). Drawing on over 9,000 Wikipedia plot summaries of 20th- and 21st-century novels, it demonstrates how LLMs can surface the implicit life lessons – or story morals – encoded in narrative summaries at scale. Building on recent work in moral inference and narrative abstraction, the study proposes a reflexive, perspectival approach that emphasizes interpretation over taxonomy. To account for the semantic variability of LLM-generated morals, the study employs a randomized prompt assignment strategy and analyzes the resulting moral keywords using co-occurrence networks and hierarchical clustering, enabling the identification of latent moral communities and comparison across modeling approaches and time. Taken together, the findings argue for the value of LLMs not only in extracting narrative values, but in enabling a new, culturally situated view of literary history through computational means.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".