Language Use by Staff Members in Saudi English Higher Education Departments: Beliefs and Gender Differences
Bibliographic record
Abstract
Educational language policies in Saudi Arabia have attracted a great deal of attention in recent years. English departments in the country are comprised of male and female staff members who practise these language policies at departmental level and also outside the domain of the classroom and on a variety of occasions. There are several reasons for the use of English (a foreign language) alongside Arabic (the mother tongue) and these have an influence on the shape of current or future language policies. The effect of gender on the selection of particular reasons has not been investigated and this is the focus of the current paper. In the study referred to here, both quantitative (online survey) and qualitative (open-ended section) approaches to data collection were adopted. The context of the study was English departments in Saudi Arabian higher education establishments. The survey consisted of five items (statements) and two questions with open-ended sections. The data was collected from different regions of the country and included male (n = 67) and female (n = 143) staff members. The Chi-Square test of independence was administered to determine the significance of differences found between the two genders and only in one of the five items was a statistically significant difference found. It was, therefore, concluded that males and females in Saudi English departments share similar beliefs with regard to the use of language, with only slight differences between them. This paper discusses the implications of these findings as well as possible areas of investigation for future researchers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".