Morphosyntactic Development in First Generation Arabic—English Children: The Effect of Cognitive, Age, and Input Factors over Time and across Languages
Bibliographic record
Abstract
This longitudinal study examined morphosyntactic development in the heritage Arabic-L1 and English-L2 of first-generation Syrian refugee children (mean age = 9.5; range = 6–13) within their first three years in Canada. Morphosyntactic abilities were measured using sentence repetition tasks (SRTs) in English and Syrian Arabic that included diverse morphosyntactic structures. Direct measures of verbal and non-verbal cognitive skills were obtained, and a parent questionnaire provided the age at L2 acquisition onset (AOA) and input variables. We found the following: Dominance in the L1 was evident at both time periods, regardless of AOA, and growth in bilingual abilities was found over time. Cognitive skills accounted for substantial variance in SRT scores in both languages and at both times. An older AOA was associated with superior SRT scores at Time−1 for both languages, but at Time-2, older AOA only contributed to superior SRT scores in Arabic. Using the L2 with siblings gave a boost to English at Time−1 but had a negative effect on Arabic at Time-2. We conclude that first-generation children show strong heritage-L1 maintenance early on, and individual differences in cognitive skills have stable effects on morphosyntax in both languages over time, but age and input factors have differential effects on each language and over time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".