Functions of Code-switching in the Egyptian EFL Settings
Bibliographic record
Abstract
The main objective of this paper is to explore the alternation between languages, or what is recognized as the code-switching (CS) phenomenon among English language instructors in Egyptian universities. This is conducted by investigating the various purposes that might urge university instructors to code-switch between English and Arabic in their class interactions. This study, therefore, probes the extent to which code-switching is dexterously used in classroom discourse to communicate particular conversational and educational purposes pertinent to the teaching and learning process. Data are taken from 10 classes taught by five university teachers (two classes each) at the English Department, Cairo University. Data are collected through audio recordings of teachers' interactions in the class, and extracts from the participants' speeches are transliterated and analyzed. There are two overarching research questions in this study: first, do Egyptian university teachers of English code-switch? Second, what are the functions of teachers’ code-switching in classroom interaction? Findings indicate that there are instances of code-switching in the discourse of all five participants at varying levels. Findings also reveal a variety of conversational and educational purposes triggering code-switching by the participants. Despite the fact that the findings revealed in this paper are solely thought to be relevant to CS among English language instructors in Egyptian universities, they can be further applied to other classroom discourses and in other EFL settings.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".