Verb repetition as a template for reactive tokens in Japanese everyday talk
Bibliographic record
Abstract
Abstract A systematic investigation into the first large scale Japanese conversation corpus reveals that repeated verbs (RVs) occurring in the response position tend to involve frequently used verbs such asaru‘to exist’ andchigau‘to differ’ (e.g.,aru aru aru). Further, longer RVs, those involving more repetition, are even more likely to occur with frequent verbs. In RVs, we find the verb having lost some of its lexical meaning and phonological substance (e.g.,chigau>chiga). RVs in fact behave more like pragmatic particles functioning as reactive tokens, i.e., short responses interjected by non-main speakers. RVs as reactive tokens are most clearly observed when they are used together with standard reactive tokens such ashai hai hai hai‘yes, yes, yes, yes’,so(o) so(o) so(o)‘yes, yes, yes’, and(i)ya (i)ya (i)ya‘no, no, no’, which also exhibit repetition and phonological reduction. Verb repetition is thus better understood as a template to turn verbs into reactive tokens.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".