Modifying plurals, classifiers, and co-occurrence: The case of Korean
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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