Examining Cross-linguistic Influence of Japanese Word Order: An Eye-Tracking Study on L2 English Learners’ Text Comprehension
Bibliographic record
Abstract
Cross-linguistic influence is a systematic and unavoidable feature of language learning. This study aimed to investigate whether prepositive word order in Japanese noun clauses can serve as a cross-linguistic influence element in the acquisition of relative clauses (a form of a postpositive modifier) in English. To test this hypothesis, this study conducted an eye-tracking experiment with native Japanese speakers and showed the following results statistically. First, Japanese participants generally paid more attention to modifier clauses than the noun phrases they modified when reading English sentences with relative clauses. Second, when interpreting accusative relative clauses, longer fixations were observed both in the modifier clauses and in the antecedents, likely due to their complexity for Japanese L2 learners of English, as evidenced by the lowest accuracy rate. Finally, and most notably, the findings revealed that the participants who had not fully acquired the relative clause construction focused more on the left side of the relative pronoun than those who had. This tendency was most prominent when interpreting subjective relative clauses, a result that supports the hypothesis that prepositive L1 modifiers could be a CLI element in the acquisition of English postpositive modifiers.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".