Bibliographic record
Abstract
This study explores how Japanese transitive/unaccusative verb pairs have transformed from being a substantive verb to the various forms they fulfil in Modern Japanese (i.e., an aspectual verb, a noun, an adjective, an adverb, a quantifier and a suffix) and how grammaticalisation and lexicalisation play an essential role during the processes. A working definition of ‘grammaticalisation’ and ‘lexicalisation’ that applies to Japanese is put forward, followed by a corpus-based investigation as well as a case study. The finding reveals that (a) the process by which a lexeme develops into a noun is a case of lexicalisation; the process by which a lexeme develops into an aspectual verb, an adverb, an adjective, a suffix or a quantifier is a case of grammaticalisation; (b) transitive verbs are more likely to convey aspect than unaccusatives are. The shift into a quantifier is limited to unaccusative verbs. Grammaticalisation (affixation) and lexicalisation in Japanese both require syntactic reduction and morphological alternation. The two differ in that lexicalisation does not require an alternation in writing, i.e., a lexicalised item can remain being written in Chinese characters (Note 1) whilst a grammaticalised item can only appear in kana script. Phonological alternation is obligatory in grammaticalisation but not required by lexicalisation. Lexicalisation appears to occur before grammaticalisation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".