Bibliographic record
Abstract
Abstract Copredication occurs when a sentence receives a true reading despite prima facie ascribing categorically incompatible properties to a single entity. For example, ‘The red book is by Tolstoy’ can have a true reading even though it seems that being red is only a property of physical copies, while being by Tolstoy is only a property of informational texts. A tempting strategy for resolving this tension is to claim that at least one of the predicates has a non-standard interpretation, with the salient proposal involving reinterpretation via meaning transfer. For example, in ‘The red book is by Tolstoy’, one could hold that the predicate ‘by Tolstoy’ is reinterpreted (or on the more specific proposal, transferred) to ascribe a property that physical copies can uncontroversially instantiate, such as expresses an informational text by Tolstoy. On this view, the truth of the copredicational sentence is no longer mysterious. Furthermore, such a reinterpretation view can give a straightforward account of a range of puzzling copredicational sentences involving counting an individuation. Despite these substantial virtues, we will argue that reinterpretation approaches to copredication are untenable. In §1 we introduce reinterpretation views of copredication and contrast them with key alternatives. In §2 we argue against a general reinterpretation theory of copredication on which every copredicational sentence contains at least one reinterpreted predicate. We also raise additional problems for the more specific proposal of implementing reinterpretation via meaning transfer. In §3 we argue against more limited appeals to reinterpretation on which only some copredicational sentences contain reinterpretation. In §4 we criticize a series of arguments in favour of reinterpretation theories. The upshot is that reinterpretation theories of copredication, and in particular, meaning transfer-based accounts, should be rejected.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".