Acquisition of aspect in L2: The computation of event completion by Japanese learners of English
Bibliographic record
Abstract
Abstract Previous studies on the acquisition of semantics in the aspectual domain have suggested that a difficult case for achieving a targetlike representation in a second language arises when learners need to preempt a first language (L1) option (Gabriele, 2009). This study investigates this issue by focusing on a learning scenario where predicate-level variability exists in the L1 input. We investigate whether Japanese learners of English can learn to invalidate event cancellation readings (Tsujimura, 2003) in English and how such knowledge develops with increasing English proficiency. We address these questions by examining how Japanese learners of English interpret accomplishment predicates that allow an event cancellation reading in Japanese but not in English. A truth-value judgment task was administered to 60 beginner, 96 intermediate, and 40 advanced Japanese learners of English as well as 20 L1 English and 20 L1 Japanese speakers. Our results showed that Japanese learners of English progressed toward a targetlike representation of aspectual entailment. We argue that such progress follows two parallel routes: a grammatical route rooted in the learners’ growing awareness of the English determiner and number morphology combined with a statistical route rooted in the learners’ inferences based on missing data.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".