Code-Switching Between Arabic and English: Reasons, Types and Attitudes as Expressed by EFL Female Students at Imam Muhammad Ibn Saud Islamic University
Bibliographic record
Abstract
This is a preliminary qualitative quantitative survey of the code-switching reasons, forms and attitudes as expressed by Imam Muhammad ibn Saud Islamic University language and translation female students. To achieve this purpose, seventy (70) students responded to an open questionnaire on the various reasons, forms of code-switching and attitudes towards code-switching during the summer semester, 2018. The results revealed that eminent among the reasons behind code-switching was the desire to speak two languages. As for the types of code-switching, these included, according to the participants, switches within the sentence (intrasentential CS), switches that occur at sentence boundary level (intersentential CS) between Arabic and English and switches at the beginning and at the end (Tag CS) of the sentence. As for the attitudes toward code-switching, these were mainly split between those who were positive towards code-switching to indicate, for instance, that the speaker is more knowledgeable and holds a higher educational qualification and rank and those who were negative towards this phenomenon. Also, the study pointed to those participants who expressed their admiration of their mother tongue having strong faith in its communicative potentiality. Other attitudes encompassed mixed attitudes towards code-switching and the need to restrict using code-switching to certain intercultural situations. Based on the results of the study, the researcher recommends that further in-depth studies are demanded to investigate the various variables that constitute this sociolinguistic behavior and how co-switching may be perceived by some as an avenue for further intercultural and global communication. Meanwhile, the pedagogical implications of CS need to be investigated.
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 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 | 0.001 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".