Bibliographic record
Abstract
Abstract Both the semantics of fictional discourse and the semantics of indexicality are canonical topics in the philosophy of language, on which there exists well-known significant literature. However, the same cannot be said for the terrain where they overlap. That is, the distinctive issues raised by fictive uses of indexicals and demonstratives have not been extensively studied per se. The aim of the present essay is to shed some light on this terrain, and to advance our understanding of some of these issues. As it happens, accounting for indexicals in fiction requires the use of innovative new tools. In particular, the standard, familiar taxonomy of types / tokens / utterances is not sufficient to account for the complex cognitive significance and truth-conditions, unique to these kinds of case. For instance: it is widely recognized that, with indexicals generally, semantic properties attach to utterances, not to types or to tokens. But in fiction there are no utterances (in the relevant sense). An innovative notion is required, which I call an “indexed token”. This account of indexicals in fiction, based on the notion of an indexed token, is developed within a Perry (2012)-inspired pluri-propositionalist framework. As such, the present essay constitutes an original application of that framework, extending its already impressive reach.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".