Semantic triggers, linguistic variation and the mass‐count distinction
Bibliographic record
Abstract
Abstract Although most languages allow nouns to be used with numerals to express cardinality, they differ significantly in how they grammatically encode such expressions. Some languages, like English, require count syntax whereas others, like Mandarin, lack count syntax and typically use classifiers. Here, the chapter asks what appears to be a simple question: how do children determine whether their language makes a distinction between mass and count syntax? This question reveals itself to be subtle and difficult when languages beyond English and Mandarin are considered. The chapter argues that prototypical syntactic and morphological differences between mass-count and classifier languages are not constitutive of this typological difference. The use of classifiers, the combination of numerals with bare nouns, and even plural morphology can occur in both mass-count and classifier languages. As a result, such features cannot be sufficient for determining whether or not a language has count syntax. Instead, the chapter argues that it is the relation of these syntactic structures to their semantic interpretations that differentiates languages and guides acquisition. Only mass-count languages can specify exclusive reference to singularities in absence of classifiers or measure words.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".