Bibliographic record
Abstract
Abstract Nearly all properties are, to a certain extent, versatile: there are many different ways to instantiate them. Consider for example a light-blue scarf and a dark-blue gemstone. These two objects share the property of being blue, despite being different kinds of objects and differing in the way in which they are blue. Our key insight (‘Property Versatility’) is that this apparently mundane observation should be extended: many properties are considerably more versatile than theorists typically take them to be. This simple insight turns out to be incredibly powerful in addressing a wide range of issues in the philosophy of language, metaphysics, and beyond. One such issue is the classic problem of copredication. Copredication sentences such as ‘Lunch was delicious but took hours’ are common yet puzzling. These sentences can be true despite seemingly ascribing incompatible properties: it appears that only the food eaten can be delicious, and only the event attended take hours. This book offers a comprehensive discussion of the problem of copredication, including a critical evaluation of extant approaches to the problem. The discussion culminates with a defence of the Property Versatility approach to copredication. Appealing to the insight behind Property Versatility, we develop a simple yet empirically powerful approach to copredication. In addition, the book demonstrates how Property Versatility is a powerful tool in addressing a wide range of issues beyond copredication, including the semantics of generics, the metaphysics of establishments and repeatable artworks, fictional discourse, and the nature of ambiguity, as well as a host of others.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".