Shared Reading of Dual Language Books Within Urdu Speaking Families: An Analysis of Code-switching in Urdu and English Languages During Reading Interactions
Bibliographic record
Abstract
Abstract Code-switching is common during bilingual conversation, and it is also inevitable in bilingual children. The occurrence of English code-switching during the conversation of Urdu-speaking families is established but has not been thoroughly researched. While the benefits of shared book reading are well-researched, shared book reading in dual-language storybooks is a novel approach. It has not been extensively explored with parents in home settings. Dual language books, or bilingual books, convey the same story in two languages. Typically, the entire book presents the text in both languages side by side (Domke, 2023). This study seeks to fill this gap by investigating code-switching in Pakistani Urdu-speaking households with 4-6-year-old children during shared reading of dual-language storybooks in English and Urdu in two locations, Canada and Pakistan. Thirty parent-child dyads from each country participated in reading sessions for three selected books. The research employed a mixed-methods approach, primarily utilizing three storybooks read by parents to their children. It aimed to understand the dynamics of bilingual interactions and code-switching through the lens of shared reading experiences. The results revealed significant effects of the story's language on language production. More code-switching into English occurred when parents read the book in Urdu. In contrast, parents often switched to Urdu while reading in English. In Pakistan, linguistic behaviour revealed frequent use of English words or switching back to English from Urdu when parents explained the story in Urdu. The most common form of code-switching was inter-sentential switching into English. However, no significant differences were observed in parents' overall language behaviour across the two locations. Interestingly, children in Pakistan frequently requested to listen to the stories in English, reflecting a preference for the school language. In contrast, half of the children in Canada preferred the story in Urdu, demonstrating pride in their heritage language. These findings align with the research of McCarthy (2018) and Muysken et al. (1996), emphasizing the influence of social context and educational language on bilingual families' language practices.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".