Code-switching in Communicationby Male and Female English-majored Students: A Case Study at Two Selected Universities in Binh Duong Province
Bibliographic record
Abstract
Nowadays, code-switching is a common and intricate phenomenon. Together with the rise of increasingly bilingual and multilingual communities, code-switching has become a critical linguistic and academic issue. The purposes of this study are to discover the features of code-switching toward male English learners on the impact of their first language and second language, to investigate the features of code-switching toward female English learners on the impact of their language (L1) and second language (L2), and to identify the differences and similarities in code-switching between male and female English learners on the impact of their first language (L1) and second language (L2). A total of 100 students from two universities in Binh Duong province was included in the study. The data were collected quantitatively and qualitatively through a mixed-method study using the tools of a questionnaire (for students) and a semi-structured interview (for teachers and students in charge of the experimental classes). According to the study results, English-majored students could only occasionally switch from L1 to L2 language during discussions and may require more effort to acquire higher IELTS scores. Furthermore, English-majored students rarely convert from their native tongue to another second language and should regard English as the classroom instruction language. However, alternating between native and L2 languages is beneficial.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".