Semantic triggers, linguistic variation and the mass‐count distinction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 it