Bibliographic record
Abstract
To what extent, and in what ways, is it possible for works of fiction to influence their readers’ ethical development? In this essay, I explore different answers to this descriptive question in philosophy and literary studies. I dub a view shared by Iris Murdoch and Martha Nussbaum as the attention account: that great works of fiction can influence their reader’s ethical development by compelling them to cultivate ethically charged attention. I then evaluate Joshua Landy’s criticism of this account and his alternative, which I dub the clarification account: that works of fiction can influence their reader’s ethical development by helping them clarify their core ethical commitments. I argue that neither the attention account nor the invitation account describes the one and only way in which works of fiction can influence their readers’ ethical development. I then ask a normative question: what ways in which works of fiction can influence our ethical development should we embrace? Drawing on Kendall Walton’s make-believe model of fictional experience, I develop an account of a third way in which works of fiction can influence their readers’ ethical development, which I call the invitation account: works of fiction can influence their readers’ ethical development by inviting them to unseat and positively revise their ethical commitments. I make the case for the invitation account by using it to analyze two contemporary novels, Rachel Cusk’s Outline and Marilynne Robinson’s Gilead. I argue that the process described by the invitation account—that is, the way of invitation—is one we should embrace.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".