The longitudinal development of L2 complex syntax in Arabic-English refugee children: sources of individual differences and comparison of measures of syntax
Bibliographic record
Abstract
Abstract We examined the growth of English-L2 clausal density (CD) in narrative language samples from 129 school-age Syrian refugee children during their first 5 years of residency in Canada. First, we found that CD showed unique developmental trajectories from MLUw, and relatively rapid acquisition, consistent with studies with non-refugee participants. Second, faster growth in CD was associated with superior cognitive abilities and higher maternal education. An older-age advantage was found at Time 1, but a younger-age advantage emerged across Time 2–3. Factors more specific to the refugee experience (time in refugee camps and wellbeing difficulties) also predicted variance in CD and MLUw development but to a lesser extent. Finally, modeling performance on sentence repetition tasks revealed stronger contributions of lexical diversity and MLUw than CD. We conclude that complex syntax is relatively resilient in the L2 acquisition of refugee children and that CD in naturalistic production and SRT capture different abilities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".