Code-Switching and English Language: A Linguistic Study in the Saudi Perspective
Bibliographic record
Abstract
There are several interpretations of code-switching. Some teachers encourage EFL learners to apply it as they believe it helps them acquire and comprehend the target language. This study investigates in three different settings the code-switching behaviors of 10 Saudi students, the association between the participants' degree of English proficiency and their employment of code-switching. It also investigates how the context affects code-switching behaviors by examining the various types of code-switch as well as how code-switchers perceive it. The study's qualitative methodology involved interviewing the participants and use of checklist to analyze their responses. Results show the participants with high English proficiency levels, did not like to switch codes. Additionally, while the minority of participants claimed they did not code-switch in the Saudi context, the remaining individuals claimed they did so in each of the three settings. However, the study found that among the participants, one word (noun) was the form of code-switch that was used the most frequently. Finally, the study demonstrated that even though all individuals occasionally switched codes, they all had unfavorable perceptions of it. The study suggests that teachers should regulate code-switching in different contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".