Investigating morphosyntactic and semantic measures in bilingual Azeri-Persian speaking children aged 5.5 to 6.5 years with and without language impairment
Bibliographic record
Abstract
The present study aims to investigate morphosyntactic and semantic measures in bilingual Azeri-Persian-speaking children aged 5.5–6.5 years with and without language impairment. In this cross-sectional study, the bilingual participants were thirty children with language impairment (LI) and fifteen typically developing children (TD) who were selected from nurseries and Speech therapy clinics. The language samples were collected through story-telling in Azeri and Persian languages, separately. The linguistic analysis was done based on morphosyntactic and semantic parameters. The Alberta Language and Development Questionnaire (ALDeQ) parent report questionnaire was completed via interviewing with the parents to differentiate language impairment from language differences. Study findings revealed a significant difference between the morphosyntactic and semantic scores in two groups of bilingual Azeri-Persian speaking LI and TD children (p ˂ 0.05). Also, the results demonstrated no significant relationship between the scores of linguistic scores and age in LI and TD children (p < 0.05). According to the result of the study, morphosyntactic and semantic parameters of language samples in bilingual Azeri-Persian-speaking children could be utilized to provide diagnostic information for speech and language pathologists in LI children among bilingual Azeri-Persian communities.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".