Bibliographic record
Abstract
Abstract This paper argues that the mass-count distinction does not represent a fundamental division between the world's languages. We demonstrate that such a distinction, as commonly defined within the linguistic literature, often conflates two facts: the semantic fact, found in all languages, that some words have atomic denotations and some do not, and the morphosyntactic fact, found in languages with contrasting singular-plural morphology, that some nouns have both singular and plural forms while others have only one such form. By comparing English with Mandarin Chinese, we discuss whether this morphosyntactic distinction might correlate with the presence or absence of a rich classifier system (as well as other types of quantification). This potential correlation has greatly influenced how linguists have investigated nominal systems across languages and it has even led some to hypothesize that morphosyntactic subcategories might determine the ways in which a grammar can “count” and “quantify.” We outline some important exceptions to this proposed correlation in languages such as Ch’ol, Mi’gmaq and Western Armenian. The paper concludes by arguing not only that there is no such correlation, but that linguists should rethink how they investigate nominal systems, focusing more on lexical variation (even within a single language) than on parametric variations across languages.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".