Gender Imbalance in Brunei Tertiary Education Student Populations: Exploring English Language, Self-Efficacy and Coping Mechanisms as Possible Causes
Bibliographic record
Abstract
Brunei has a multilingual society where no citizens (except very young children) are monolinguals. The dominant and official language of the country is Bahasa Melayu. English is widely spoken by many bilinguals and used as the medium of instruction in most schools in addition to being an admission criterion to colleges and universities. Arabic, the language of both Islamic educational institutions and religion, is added to trilinguals who are adherents while each indigenous language is used by most of the multilinguals. In such a complex linguistic environment, the learning and use of spoken or written English is often affected by both retroactive and proactive interferences from other competing languages. In the present survey (N = 287) females scored significantly higher on an English test than their male counterparts. In addition, females significantly used the emotion-oriented coping strategy more than the male peers. No significant differences were obtained on self-efficacy variable by gender and ability in English. Similarly, no significant differences were also obtained on coping strategies by ability in English. However, the task-oriented and avoidance-oriented coping styles were predictors of good and bad achievement in English respectively. Overall, English appeared to be a partial causal factor to the gender gap in Brunei tertiary student populations. Further mixed-methods research was recommended to access more details and possible solutions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".