Towards Linguistically-Responsive Literacy Assessments in Bilingual Children: Establishing an Urdu Phonological Tele-Assessment Tool (U-PASS)
Bibliographic record
Abstract
Childhood literacy contributes to future academic and socio-economic success; it is therefore important to provide early reading intervention. Successful intervention hinges on early assessment. Speech-language pathologists and educators use early literacy precursor assessment tools to identify children at risk for future reading difficulties. Phonological processing is one of these precursors commonly assessed with monolingual and bilingual children. However, there is a clear English-language assessment bias when it comes to these tools, which impacts clinical and research practices. To address this bias, we developed an age- and linguistically-appropriate Urdu Phonological Tele-Assessment tool (U-PASS) across three phases: (1) Item & Subtest Structure Development, (2) Item Revisions, and (3) Tool Evaluation, including subtest and item-level analyses based on average accuracy rates and discrimination power. We also examined the U-PASS tool’s criterion-based concurrent validity in relation to the Urdu word/non-word reading accuracy skills of 115 typically-developing Urdu-English simultaneous bilinguals in Grades 1-2 across Canada and Pakistan. Linear regression analyses indicate significant phonological processing-reading correlations after accounting for expressive vocabulary, language background and demographic factors, thereby demonstrating tool concurrent validity. This open-access tool will facilitate literacy assessment in under-investigated heritage languages commonly spoken by bilinguals, and promote literacy accessibility 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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".