Developing Language-specific Screening Tools: Assessing Phonological Awareness Skills in Urdu-English Bilingual Children
Bibliographic record
Abstract
Childhood literacy is a major contributor to future academic and socio-economic success. It is therefore important to provide early reading intervention, via literacy precursor screening tools that can detect potential reading difficulties. Our systematic review and meta-analysis highlighted phonological awareness and vocabulary as commonly assessed literacy precursors that are consistently associated with (and in many cases predict) reading abilities in bilinguals (Chapter 2). To address the English-language assessment bias evident in our review, I developed an age- and linguistically-appropriate Urdu phonological awareness test (Chapter 3). I assessed 95 typically-developing Urdu-English simultaneous bilinguals, in Grades 1-2, across Canada and Pakistan on Urdu and English phonological awareness, expressive vocabulary and word/non-word reading measures. Simple and multiple linear regression analyses indicated significant within-language associations between the novel Urdu phonological awareness test and word/non-word reading, thereby demonstrating criterion-based validity. The developed Urdu phonological awareness test will facilitate early literacy screening in Urdu and linguistically-related languages.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".