Gender and Civil Engineering in Higher Education: The Case of Mauritius
Bibliographic record
Abstract
Engineering plays a crucial role in everyday life and is the backbone of growth and development of the world including Mauritius. To embrace development, higher education institutions have to ensure that students are equipped with appropriate knowledge and skills to meet the needs of the country. Unfortunately, data shows that there is an underlying gender disparity in civil engineering training in higher education. It is imperative to understand the causes of gender inequity in engineering in higher education. This paper summarises the findings obtained from in-depth critical individual conversations with three participants, which explored the under-representation of undergraduate female students in a civil engineering degree in a higher education institution in Mauritius. The aim was to find ways in which the recruitment of female students in higher education and advancement of women in the field of civil engineering can be achieved. The findings support the ‘non-visibility’ of civil engineering as a field to study by women and the need to fit in as an engineer by women. As a result of the research, recommendations were made to assist policy and decision makers to develop evidence-based policies to address gender inequity in engineering in higher education.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".