Multiple subject constructions in Japanese and the development of AGRP in L2 English
Bibliographic record
Abstract
Starting from the assumption that Japanese has no subject–verb agreement, this paper focuses on the acquisition of agreement, specifically on structure building of a functional category AGRP (agreement phrase) which provides a configuration in which nominative case is licensed/checked in a bi-unique SpecAGR relation. L2 clausal structures corresponding to Japanese multiple subject sentences receive particular scrutiny since the possibility of licensing more than one subject phrase is expected to influence L2 implementation of an AGRP in this context. Relying on a written corpus, the paper outlines a transition to a grammar with AGR, drawing on lexical learning (Clahsen, Eisenbeiss and Penke 1996), structure-building (Vainikka and Young-Scholten 1996, 1998) and elements of constructionism (Herschensohn 2000). The data indicate that Japanese speakers create a bi-unique spec-head relation for agreement. However, in clauses corresponding to multiple subject sentences, instances of failed agreement suggest that co-indexing is not yet consistently carried out with the phrase in SpecAGR. Also, instances of inappropriate predication and caseless DPs indicate that creation of AGRP does not bring about an immediate solution to the problem of integrating the multiple subject phrases in Japanese into English clausal structure.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".