Classifiers, partitions, and measurements: Exploring the syntax and semantics of sortal classifiers
Why this work is in the frame
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Bibliographic record
Abstract
In many languages, measure terms like item and kilo, as in two items of furniture and two kilos of rice, can be used either to partition the nominal denotation into countable units, or to measure a denotation without inducing a partition. These two types of measurements are associated with two different syntactic structures: a partition-structure where the measure term forms a constituent with the noun independent of the numeral, and a measure-structure where the measure term forms a constituent with the numeral. Some researchers have claimed that in classifier languages, sortal classifiers are (most often) used in a partition-structure—hence the classifier forms a constituent with the noun independent of the numeral. In contrast, non-sortal classifiers (i.e., measure classifiers) are often used in a measure-structure—the classifier forms a constituent with the numeral and this constituent modifies the noun. Contrary to these claims, we demonstrate that in Ch’ol (Mayan) all classifiers, sortal and non-sortal alike, are used in a measure-structure independent of the types of readings that are available with respect to the measure term. As a result, the correlation between partitioned meanings and partition-structures is not universal. We review several diagnostics that support this claim. These diagnostics can be used as a template to test the constituency structure in other classifier languages.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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