More evidence on the unergative–unaccusative distinction in second language grammars
Bibliographic record
Abstract
Abstract This study presents new evidence for the structural unergative–unaccusative distinction, in second language (L2) grammars, focusing on elementary-level Japanese-speaking learners of English (JLEs). The underlying distinction of unergatives–unaccusatives is often obscured on the surface strings due to independent syntactic properties such as feature-driven subject movement (in English) or headedness (in Japanese). Nevertheless, based on previous findings, elementary-level JLEs are expected to have reset headedness but have not acquired subject movement. Then, the resulting representation would not involve the properties obscuring the underlying unergative–unaccusative distinction and potentially exhibit it on the surface strings in L2 English. Following these observations, we carefully designed test sentences with un/grammatical word orders that elementary-level JLEs would generate and conducted an acceptability judgment task with native speakers of English and elementary-/intermediate-level JLEs. The results showed that, in contrast to native controls and intermediate learners, who exhibited target-like patterns, elementary-level JLEs incorrectly accepted ungrammatical word orders only with unaccusatives (e.g., * When did arrive the train ? ) but not those with unergatives (e.g., * Where did dance the man ? ). This discrepancy can be attributed to the sensitivity to the structural distinction of unergative–unaccusative verbs, and our data provide evidence for the creative construction of an interlanguage in L2 acquisition.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".