Bibliographic record
Abstract
This paper provides a corpus-linguistic study on subjectivity in Japanese, in an effort to arrive at how subjectivity, transitivity and grammaticalisation are related. 899 lexicons from nine grammatical categories (suffixes and prefixes, adjectives, particles, auxiliaries, nouns, adnominals, adverbs, and transitive/intransitive verb pairs) are examined. The findings reveal that Japanese is a subjective/objective-split language, and that subjectivity in affixes is facilitated by phonology: voiced/voiceless consonant alternation. The data also show that consonant-voiced prefixes and suffixes yield a subjective reading, while consonant-voiceless prefixes and suffixes render an objective meaning. Split subjectivity in adjectives is realised by morphology: しい-ending adjectives tend to be subjective, while い-ending adjectives are mostly objective. The differentiation of subjectivity in adjectives is further tied to the constraints on personal pronoun and verbalisation possibilities. Intriguingly, objective/subjective readings of しい-ending adjectives andい-ending adjectives are switchable. Furthermore, among transitive/intransitive verb pairs, intransitive verbs are likely to get grammaticalised, while transitive verbs are likely to be lexicalised and thus render a subjective reading. This is confirmed by change-of-state verbs and motion verbs. This paper therefore puts forward the hypothesis that the interrelationship of grammaticalisation and lexicalisation is orthogonal.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".