Causes of Gender Differences in Accounting Performance: Students’ Perspective
Bibliographic record
Abstract
This study employs the survey method to investigate the factors that cause academic differences between female and male students at the largest university in Botswana. The population of this research was the students of the last three years of the 4 year Bachelor of Accountancy degree programme at the University of Botswana. Anchored on the prior studies’ indications that female students outperform their male counterparts in accounting examinations, the current study sought the views of the respondents on factors responsible for this phenomenon and their suggestions on how the gap may be bridged. This study revealed that the key factor explaining academic performance is individual’s commitment and right attitude towards accounting studies. Respondents believe that female students perform better because they work harder and have better study ethics. Females attend more classes and tutorials, seek guidance on their studies from lecturers and participate more in class discussions than their male counterparts. Male students perform poorly because they lack enthusiasm towards studies and fail to balance social life and academic work while at school. The implications of this study are that male students need to re-examine their attitude towards education, class attendance and participation in academic activities in order to improve their grades. The society and the educational institutions need to become more vigilant in ensuring that males remain focused on positive learning while in school.
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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 | 0.005 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".