Cross-Language Transfer in Cross-Country Contexts: Examining Longitudinal Relationships between Urdu Phonological Processing and English Reading in Pakistan and Canada
Bibliographic record
Abstract
Purpose: This study examines whether kindergarten-level Urdu phonological processing predicts the future Grade 1 English word and non-word reading accuracy skills of Urdu-English bilinguals in (i) Pakistan, with an Urdu as a national/societal-language country context, and (ii) Canada, with an Urdu as a heritage-language country context. Method: At Timepoint 1 of this longitudinal study, we assessed 154 Urdu-English kindergarten-aged bilinguals in Pakistan (n = 104) and Canada (n = 50) on their Urdu phonological awareness and rapid automatized naming (RAN) skills, via the Urdu Phonological Tele-Assessment Tool (U-PASS). After 1 year at Timepoint 2, we tested their English word and non-word reading accuracy skills at the Grade 1 level. Result: Our hierarchical linear regressions consistently demonstrated cross-language transfer between Urdu phonological awareness and the English word and non-word reading accuracy measures in both Pakistan and Canada. Predictive strength differences were demonstrated between RAN and reading outcomes, based on country-specific contexts. Conclusion: Our findings demonstrate that languages learnt in both a societal or heritage context (i.e., Urdu) contribute to early reading skills in another language (i.e., English), thereby emphasizing the importance of overall cross-language knowledge at the early stages of learning to read. This study also emphasizes the role of cross-language transfer for facilitating equitable access to early assessment for bilingual populations: Urdu phonological processing skills can be used to identify bilingual children’s future English reading abilities, rather than waiting until the child demonstrates adequate English language proficiency for completing traditional English phonological processing assessments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".