Is object relative clause comprehension particularly sensitive to quantity of language exposure in sequential bilingual children?1
Bibliographic record
Abstract
Research on the role of language exposure in child second language acquisition suggests that certain language properties are more sensitive to variation in exposure than others. Understanding which language properties are more or less sensitive to exposure is important for improving the clinical language assessment of these learners. This study reports on object relative clause comprehension in 23 first graders enrolled in an early total French immersion programme in English-speaking Canada. These sequential bilingual children have limited exposure to French compared to English (i.e., about 3 months of cumulative French exposure). The main objective was to see whether these learners would have similar object relative clause performance in both languages, despite the uneven exposure pattern. Mixed-effects logistic regression modelling revealed that structure (object relative clause vs. subject relative clause) was a stronger predictor of comprehension than the language of the task. Individual data analyses revealed that most children were either above chance in both languages or at chance in both languages, thus suggesting that ORC performance in one language predicts it in the other. These results suggest that targeting relative clauses in clinical assessment may lead to tools that are less biased against unbalanced bilinguals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".