Bibliographic record
Abstract
ABSTRACT Everett (2005) has argued that fictional realism runs into insuperable difficulties when faced with fictional stories in which there are indeterminate identities. By appeal to a principle linking the individuation of characters within stories and without, Everett argues that such stories entail that there are indeterminate identities outside of fiction on the fictional realist picture. And although indeterminate identities are perfectly acceptable within fiction, they are intolerable in the (nonfictional) world itself. In this paper, I develop the “extended-game” model of fiction according to which fictional characters are props—and, hence, potential objects of reference—in authorized games of make-believe for fictional works in which they appear but do not originate but are neither props nor potential objects of reference in authorized games for works in which they do originate. And I argue that this entails that Everett’s linking principle, on which his argument depends, simply fails to apply.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".