Text Complexity Modulates Cross-Linguistic Sentence Integration in L2 Reading
Bibliographic record
Abstract
Cross-linguistic influences (CLI) in first-language (L1) and second-language (L2) reading have been widely demonstrated in experimental paradigms with adults at the word and sentence levels. However, less is known about CLI in adolescents during naturalistic text reading. Through eye-tracking and behavioral measures, this study investigated expository reading in functionally English monolingual and Spanish (L1) - English (L2) bilingual adolescents. In particular, we examined the role of L1 (Spanish) sentence integration skills among the bilingual adolescents when L2 school texts contained challenging syntactic structures, such as complex clauses, elaborated noun phrases, and anaphoric references. Results of generalized multilevel linear regression modeling demonstrated CLI in both offline comprehension and online eye-tracking measures that were modulated by school text characteristics. We found a positive relationship (i.e., facilitation) between L1 sentence integration skills and L2 English text comprehension, especially for passages with greater clause complexity. Similar main, but not modulatory, effects of sentence integration skill were found in online eye-tracking measures. Overall, both language groups appeared to draw upon similar reading component skills to support reading fluency and comprehension when component skills were measured only in English. However, differential patterns of association across languages became evident when those skills were measured in both L1 and L2. Taken together, our findings suggest that bilingual adolescents’ engagement of cross-linguistic resources in expository reading varies dynamically according to both language-specific semantic knowledge and language-general sentence integration skills, and is modulated by text features, such as syntactic complexity.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".