Bibliographic record
Abstract
Abstract The book seeks to resolve the so-called ‘problem of mass nouns’ — a problem which cannot be resolved on the basis of a conventional system of logic. It is not, for instance, possible to explicate assertions of the existence of air, oil, or water through the use of quantifiers and variables which take objectual values. The difficulty is attributable to the semantically distinctive status of non-count nouns — nouns which, although not plural, are nonetheless akin to plural nouns in being semantically non-singular. Such are the semantics of a non-singular noun, that there can be no such single thing or object as the thing of which the noun is true. However, standard approaches to understanding non-singular nouns tend to be reductive, construing them as singular expressions — expressions which, in the case of non-count nouns, are true of ‘parcels’ or ‘quantities’ of stuff, and in the case of plural nouns, are true of ‘plural entities’ or ‘sets’. It is argued that both approaches are equally misguided, that there are no distinctive objects in the extensions of non-singular nouns. With plural nouns, their extensions are identical with those of the corresponding singular expressions. With non-count nouns, because they are not plural, there can be no corresponding singular expressions. In consequence, there are no objects in the extensions of non-count nouns at all. In short, there are no such things as instances of stuff: the world of space and time contains not merely large numbers of discrete concrete things or individuals of diverse kinds, but also large amounts of sheer undifferentiated concrete stuff. Metaphysically, non-singular reference in general is an arbitrary modality of reference, ungrounded in the realities to which it is non-ideally or intransparently correlated.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.082 | 0.037 |
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".