Factors Influencing Women's Underrepresentation in Engineering: A Literature Review at EDUCON
Bibliographic record
Abstract
In the context of women's integration into the working domain, the gradual reduction of the gender gap across numerous professional fields continues to be a reality. However, it is imperative to underline that the attainment of full gender equity across professional domains, including engineering, remains an unfulfilled objective. This document is aimed at presenting a general view of the gaps regarding women in engineering through a literature revision of the works included in the proceedings of IEEE EDUCON, to identify the factors that may still underlie the low representation of females in engineering and provide guidance to this conference stakeholders for upcoming studies and publications on this topic. The results indicate that factors such as entry and affordability hurdles, inadequate training and built-in prejudices, along with prevalent sociocultural norms, constrain greater diversity and inclusiveness of females in engineering majors. As such taking action now aiming to eliminate the gender gap by reversing these trends may result in the desired outcome. It may also provide new sources of global economic growth and support the implementation and achievement of sustainable development and inclusive growth. Finally, the literature review highlights the importance of going beyond the diagnostic phase while identifying lines of work to be strengthened.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".