Exploration into a new understanding of ‘zero anaphora’ in Japanese everyday talk
Bibliographic record
Abstract
Abstract This chapter examines the phenomenon called ‘zero anaphora’ in Japanese where syntactic arguments, thought to be projected by the predicates, are assumed to be deleted yet their referents are still tracked. A close inspection of representative narrative and interactive segments reveals that everyday talk, the primordial form of language, is carried out largely through more or less fixed expressions which are better analyzed as not projecting syntactic arguments. This suggests that deletion of arguments and tracking of referents might not be relevant to the grammar of Japanese everyday talk. We demonstrate this by discussing several facts including: (1) inserting what might be thought of as ‘deleted’ arguments in relevant examples makes them consistently more marked, awkward, or even unacceptable and (2) ‘deleted’ arguments are often associated with multiple equally possible referents, or no referents. The predominance of fixed expressions in our data suggests that they constitute the basic type of language in Japanese everyday talk. It is hoped that the current study is a contribution to building a model of grammar which captures this very characteristic of everyday talk where (semi-) fixed structure continuously emerges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".