Classifiers can be for numerals <em>or</em> nouns: Two strategies for numeral modification
Why this work is in the frame
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Bibliographic record
Abstract
This paper compares two families of theories for numeral classifiers drawing on fieldwork data from two languages, Ch’ol (Mayan, Mexico) and Shan (Kra-Dai, Myanmar). We discuss classifier-for-numeral theories and classifier-for-noun theories, which we argue make different predictions based on the syntactic position and semantic contribution of the classifier in each set of theories. We argue that Ch’ol is a classifier-for-numeral language and Shan is a classifier-for-noun language. This analysis attributes the distinction between classifier-for-numeral and classifier-for-noun languages to cross-linguistic variation in the strategies for numeral modification. The proposed diagnostics are based on the semantic role of the classifier in numeral modification and can be used to distinguish between the two types of numeral classifiers across other 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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