The Sociolinguistic Significance of the Attitudes towards Code-Switching in Saudi Arabia Academia
Bibliographic record
Abstract
Code-switching between Arabic and English marks a significant linguistic change in the history and use of Arabic in Saudi Arabia. Any kind of language change, which is an inevitable process in almost every world language, has always been resisted in Saudi Arabia mostly due to a national identity and religious factors. The current study investigated the attitude of the Saudi academia comprising English language instructors and English major students towards code-switching between Arabic and English. The study examined the perceptions of the academia towards the use of varying languages and the attitude that resulted from a perception. A sample size of 10 instructors and 40 students from four universities in the Riyadh region of Saudi Arabia was taken for the purpose of carrying out this qualitative study. Focus Group and interview methods were used to collect data and a content analysis technique was adopted to analyze their transcripts. Findings and Results indicated that there was a close relationship between education and age on one side and the acceptability of code-switching on the other. Positive attitudes towards code-switching were found among the younger participants in their tertiary level of education. The results also revealed that such an attitude affected learners' academic performance since the learners attitude towards each language contributed to their learning and knowledge acquisition.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".