The interplay between syntactic and morphological comprehension in heritage contexts: The case of relative clauses in heritage Syrian Arabic
Bibliographic record
Abstract
Abstract Previous studies show that even though monolingual children find subject relatives easier than object relatives, their comprehension of object relatives can be facilitated by morphological cues. Given that in heritage contexts functional morphology is a vulnerable domain, a question that needs to be addressed is whether bilingual children, who are heritage speakers of their L1, will also be able to use morphological cues to comprehend complex syntax. To contribute to this line of research, we focused on monolingual (N = 18; Mean Age: 11.43) and bilingual/first generation (N = 108; Mean Age: 11.98), Syrian Arabic-speaking children in Canada, and examined their ability to use gender morphology in their comprehension of relative clauses, while taking into consideration cognitive, environmental, and age-related variables. To this end, we used two offline sentence-picture matching tasks targeting relative clauses and gender (as encoded in SV agreement and object clitics). Results showed that, like monolingual children, first-generation, Arabic-speaking children living in Canada used morphological cues to comprehend complex syntax in their L1. Furthermore, even though there was an association between comprehension of gender agreement and comprehension of relative clauses, performance in gender agreement was higher than performance in relative clauses, suggesting that challenges with complex syntactic structures are not necessarily an epiphenomenon of a morphological deficit.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".