Bibliographic record
Abstract
Japanese adjectives have received a fair amount of attention for their intriguing morphological and diachronic properties. Adjectives have also been discussed in the typological literature, largely in terms of their status as a lexical category vis-à-vis nouns and verbs. Rather little research has been done, however, on the everyday use of adjectives in Japanese conversation. In our paper, we aim to show that (a) adjective usage in conversation is intricately bound up with fixedness and frequency; (b) a usage-based approach reveals that interactional and cognitive practices are deeply intertwined in this lexical category for Japanese speakers; (c) these facts reflect the nature of human language as an emergent phenomenon. Based on a substantial corpus of Japanese conversations, we find that (a) attributive adjectives are very rare; (b) among predicative adjectives, as well as the rare attributive adjectives, the most frequently occurring forms strongly tend to be associated with various types of fixedness, demonstrating its central status in our attempt to represent the grammar for real speakers.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".