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/non-word reading accuracy skills of Urdu-English bilinguals in Pakistan and Canada.Method At Timepoint 1 of this longitudinal study, we assessed 154 Urdu-English kindergarten-aged bilinguals in Pakistan (n = 104; Experiment 1) and in our exploratory study in Canada (n = 50; Experiment 2) on their Urdu phonological awareness and rapid automatised naming skills via the Urdu Phonological Tele-Assessment Tool. At Timepoint 2, we tested their English word and non-word reading accuracy skills at the Grade 1 level.Result Hierarchical linear regressions generally demonstrated significant cross-language transfer effects between Urdu phonological awareness and the English word/non-word reading accuracy measures in both Pakistan and Canada. Predictive strength differences were demonstrated between rapid automatised naming 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). This study emphasises the role of cross-language transfer for facilitating equitable access to early assessment for bilingual populations. We highlight that Urdu phonological processing skills can be used to identify bilingual children’s future English reading abilities, rather than waiting until the child demonstrates adequate language proficiency for completing traditional English phonological assessments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".