Prereading Assessment in Two Bilingual Contexts: Examining Predictive Validity of the Urdu Phonological Tele-Assessment Tool in Pakistan and Canada
Bibliographic record
Abstract
Early assessment of reading abilities is important for ensuring long-term academic and career-related success. Pre-reading assessment tools can identify children at risk of future reading difficulties. However, most bilingual children do not have access to appropriate early reading assessments. This is due to the lack of language-appropriate assessment tools. The current longitudinal study examines the predictive validity of a 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. Specifically, we investigate whether kindergarten-level Urdu phonological awareness and RAN skills predict the future Grade 1 Urdu reading accuracy and fluency skills of Urdu-English bilinguals in two bilingual contexts: Pakistan (where Urdu is spoken as a national language; n = 104; Experiment 1) and Canada (where Urdu is spoken as a heritage language; n = 50; Experiment 2). 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/non-word reading accuracy and fluency, while RAN was a reliable predictor of the reading fluency measures. The U-PASS tool provides access to linguistically- and culturally-responsive early pre-reading assessment, and enables researchers, speech-language pathologists, and educators to examine pre-reading skills in the heritage-language of Urdu-speaking children 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".