Modifying plurals, classifiers, and co-occurrence: The case of Korean
Bibliographic record
Abstract
This paper argues that the Korean plural marker –tul is best analyzed as a modifier to the nP projection, rather than as a head in the nominal extended projection such as Num or Div(ision), which a standard pluralizer (e.g., English –s) realizes. As a modifier, plural –tul bears the privative feature [plural], rather than the binary feature [±plural] reserved for a plural that realizes a head. Supporting evidence comes from the fact that the presence of –tul leads to an obligatorily plural reading, while a number-neutral reading obtains in its absence; –tul also shows no evidence of inflectional properties. Appearing as an adjunct to nP, –tul shows certain idiosyncrasies, such as irregularities in the range of nouns that it can occur with. Evidence against the common claim that –tul is associated with a definite reading is provided, which suggests that it cannot realize D or adjoin to DP. The major consequence of this paper is that the often observed non-co-occurrence of classifiers and plural markers is predicted only when the relation between the two morphemes is in syntactic complementary distribution, but may not be when the relation is in merely semantic complementary distribution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".