Classifiers, partitions, and measurements: Exploring the syntax and semantics of sortal classifiers
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.004 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".