Comparing Phonological Processing Contributions to Reading Across Orthographic Depth: Urdu-English Biscriptal Bilinguals
Bibliographic record
Abstract
Purpose: Current reading research remains Latin script-centric in a monoliterate context, and may not be generalizable to the significant percentage of school-aged children growing up as biscriptal bilinguals globally. To address this, we examined the early stages of reading development in bilingual readers of an under-studied language combination that differs across orthographic depth and script. Specifically, our longitudinal study compared within-language contributions of three phonological processing skills to reading outcomes in Urdu-English bilingual children. Method: We assessed 154 Urdu-English readers in Pakistan and Canada on their phonological awareness, phonological memory, and rapid automatized naming (RAN) skills at Timepoint 1 (Senior Kindergarten) and word/non-word reading accuracy and fluency outcomes at Timepoint 2 (Grade 1) in Urdu (orthographically-transparent language with Perso-Arabic script) and English (orthographically-opaque language with Latin script). Results: Our multivariate multiple linear regression analyses demonstrate the importance of phonological awareness and RAN for reading, across orthographic depth/script and type of reading outcome measure. Predictive strength differences were also evident. Phonological awareness accounted for greater variance in the reading accuracy measure across both languages and particularly in Urdu, with RAN contributing greater variance to Urdu reading fluency. Phonological memory was not a significant predictor in either language. Conclusions: Our study contributes to the generalizability of dominant reading models, such as the Psycholinguistic Grain Size Theory (PGST), Orthographic Depth Hypothesis (ODH), and Speed-of-Processing theoretical account of RAN-reading fluency relationship, beyond monoliterate readers of Latin scripts. Particularly, we demonstrate the importance of phonological awareness and RAN for reading outcomes in biliterate readers of a transparent orthography with a Perso-Arabic script.
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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) | 0.002 | 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".