Prereading Assessment in Two Bilingual Contexts: Examining Predictive Validity of the Urdu Phonological Tele-Assessment Tool in Pakistan and Canada
Bibliographic record
Abstract
Purpose: Early assessment of prereading abilities is important for ensuring long-term reading, academic, and career-related success. Speech-language pathologists and educators commonly use prereading assessment tools to identify and support school-aged children's future reading abilities. However, most bilingual children, including Urdu–English bilinguals, do not have access to appropriate early prereading assessments within the school/educational system. This is due to the lack of language-appropriate prereading assessment tools. The current longitudinal study examines the predictive validity of the linguistically and culturally responsive Urdu Phonological Tele-Assessment (U-PASS) tool, including subtests for phonological awareness and rapid automatized naming (RAN), commonly assessed reading precursors. Method: Specifically, we investigated whether kindergarten-level Urdu phonological awareness and RAN skills predict the future Grade 1 Urdu reading accuracy and fluency skills of Urdu–English simultaneous bilinguals in two language contexts: in Pakistan (where Urdu is spoken as a national language; n = 104; Country Context 1) and in our exploratory study in Canada (where Urdu is spoken as a heritage language; n = 50; Country Context 2). Results: Hierarchical linear regression analyses demonstrate predictive validity of the U-PASS tool across the two country contexts. Particularly, Urdu phonological awareness emerged as a consistent longitudinal predictor of Urdu word/nonword reading accuracy and fluency, while RAN was a reliable predictor of the reading fluency measures. Conclusion: The U-PASS tool provides access to linguistically and culturally responsive early prereading assessment and enables speech-language pathologists and educators to examine prereading skills in the heritage language of Urdu-speaking children across classrooms globally.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".