What can Japanese conversation tell us about ‘NP’?
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
Abstract Our examination of Japanese everyday conversation reveals that a majority of candidate NPs cannot be established as NPs based on traditional criteria, i.e., marking by particles and modification, since they are generally unmarked and unmodified. We examine these cases to reveal the difficulty of determining what to consider an ‘NP’ when analyzing everyday interaction. Our findings question the assumption of NP as a universal category, and in particular cast doubt on the theoretical importance given to the category NP for Japanese in the literature. We recognize instead the quantitatively most frequent cases as the norm, with the minority, more ‘ornamented’, instances as requiring an account. Our study advocates routinely challenging assumed categories arising from our inherited written-language, English-dominated, imagined-data linguistic tradition, and instead shifting our descriptive and theoretical focus to understanding and accounting for the majority instances, in our case the role of unmarked nominals in a grammar of conversation.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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