Cross-Linguistic Transfer of Phonological Awareness and Reading Skills in Russian-Uzbek Bilingual Children
Bibliographic record
Abstract
ABSTRACT Background Bilingualism and biliteracy impact the development of phonological awareness and reading. However, existing research is Indo‐European‐centric, limiting our understanding of reading development in diverse linguistic environments. Method Addressing this gap, this study examined the relation between phonological awareness and reading in monolingual and bilingual/biliterate children in Uzbekistan, where both Uzbek and Russian are widely spoken. One hundred two Uzbek–Russian bilingual and 98 Uzbek monolingual (6–10 years; Grades 1–4) children in Uzbekistan completed phonological awareness and reading tasks. All children attended a bilingual school; however, the language of instruction was primarily Uzbek for Uzbek children and Russian for Uzbek–Russian bilinguals. Results Overall, children demonstrated stronger reading skills in their respective language of instruction; however, bilingual children showed stronger reading skills than monolinguals, despite being instructed in their second language. This suggests that bilingual children demonstrate a reading advantage when tested in instructional languages, but not in non‐instructional languages. Bilinguals' phonological awareness significantly predicted cross‐linguistic reading performance, specifically from the instructional language (Russian) to the non‐instructional language (Uzbek). Conclusion Phonological awareness is a significant cross‐linguistic predictor of reading skills, even when the languages have overlapping but competing orthographies, providing support for interactive cross‐linguistic transfer in reading.
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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 | 0.001 | 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) | 0.002 | 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".