The Role of Context in Learning to Read Languages That Use Different Writing Systems and Scripts: Urdu and English
Bibliographic record
Abstract
Language learning involves linguistic and societal factors that interact to facilitate or hinder second language learning. Different contextual factors provide an opportunity to examine and understand the similarities and differences that occur among bilingual children who learn the same first (L1) and second language (L2) in different countries and contexts. This paper explored the role of context, learners’ profiles and linguistic differences of Urdu–English bilinguals in Canada and Pakistan. Within- and cross-linguistic comparisons were conducted for 76 Urdu–English speakers from Pakistan and 50 participants from Canada. Children, ages 8–10 years, were tested on language and literacy measures in both languages. Group comparisons of performance on language measures across languages and countries confirmed that relative strengths were based on the societal languages of each country (Urdu in Pakistan and English in Canada). Despite some similarities in relations among skills within language, differences in the language learning context provided interesting findings regarding the role of L1 skills for acquiring L2 reading skills. These findings challenge the theories developed using data from L2 learners, where learners acquire the societal language in immersion contexts, such as in North America or Europe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".