On the complementary distribution of plurals and classifiers in East Asian classifier languages
Bibliographic record
Abstract
Abstract It is widely recognized that plural morphemes and classifiers are in complementary distribution, being unable to co‐occur. Recent literature suggests a syntactic account for complementary distribution: A plural morpheme and a classifier realize the same functional head, and thus, they cannot co‐occur. The goal of this article is to examine whether this syntactic approach to the alleged complementary distribution is applicable to certain classifier languages. We review analyses for each of 3 classifier languages, Chinese, Japanese, and Korean, where a plural and a classifier co‐occur. The reviewed analyses suggest that plural markers in these classifier languages do not realize the same head with classifiers (e.g., a plural instantiates Num/D in Chinese differently from a classifier), which accounts for its co‐occurrence with a classifier. This paper also discusses other approaches to the complementary distribution of plural morphemes and classifiers, for example, a typological view and a semantic view, and concludes that they may not account for the data in the languages under discussion.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".