Gender Differences in Academic Achievement in Saudi Arabia: A Wake-Up Call to Educational Leaders
Why this work is in the frame
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Bibliographic record
Abstract
Educational leaders must consider equity in education as a priority to make sure all students receive the best education possible. Studies on this topic in Saudi Arabia, however, are still in the embryonic stage. This article, thus, examines whether significant differences in academic achievement exist between male and female students based on gender, subject value, and expectations of education attainment. From a sample study of 3,759 students, the findings showed that female outperformed male students in both math, science, and their domains. Further, the more students value a subject or expect to go far in their education, the higher the score for both students, but female still outperformed male students. Educational leaders should considerthese findings a wake-up call to the persistent academic achievement disparities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it