“His Favorite Things Mahi Al-hadia” Social Functions of Code Switching in Bilingual Children’s Conversations
Bibliographic record
Abstract
This study explored the occurrence of code switching among six Arabic-English Saudi bilingual children living in the United States at the time of the study. A Qualitative research design, using three research instruments namely, parental questionnaire, language portraits, and recorded storytelling sessions, was conducted in order to investigate the social functions of code switching. The study adopted Myers-Scotton’s (1993) Markedness Model to examine better the social motivation behind code switching in children’s conversations. Overall, the findings revealed the participant’s dominant and preferred language to be English, and the switch to English was frequent to serve certain functions, such as to change the addressee, engage in interaction, make alignment, ask for translation, expand, invoke authority, and finish the conversation. Moreover, this study contributes to the current research on the Markedness Model among bilingual children by providing evidence for Myers-Scotton (1993) as marked and unmarked code switching was observed among the Arabic-English bilingual children. This study also agrees with previous studies (e.g., Bolonyai, 2005; Fuller, Elsman, & Self, 2007; Myers-Scotton, 2002) that argued that bilingual children are rational and social actors who choose a given code intentionally to achieve certain social goals in a given interaction.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".