Prereading Assessment in Two Bilingual Contexts: Examining Predictive Validity of the Urdu Phonological Tele-Assessment Tool in Pakistan and Canada
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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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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 | 0.001 | 0.000 |
| 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 it