The Subtle Ostracism Faced by Women in Engineering: Psychological Effects of Learning in a Predominately Male Field
Bibliographic record
Abstract
In engineering, there are many obstacles that women face ranging from underlying stereotypes to physical restrictions in certain environments. The deficit of women in University programs has created a hurdle for young women entering the field of engineering. The objective of this study was to identify the challenges experienced by female undergraduate and graduate students that contribute to the systemic issue of inequity. A total of 372 male and female students actively participated in a 21-question survey featuring both multiple-choice answers as well as open-ended questions. Three themes emerged relating to culture (built environment and attitudes), gender (stereotypes and lack of role models), and personal (sense of belonging and the imposter syndrome). It was found that the built environment created physical barriers, while the attitudes of male peers, teaching assistants, and professors led to negative experiences, limiting female student success. Comments made about gender disparities focused on stereotypes and the lack of role models, which were later determined to influence students’ sense of belonging and feelings of imposter syndrome. Over half of the female students who participated in the survey felt discouragement due to the lack of gender diversity that has further impacted their experiences and education. This research may not be reflective of the experience for all females in engineering but does reflect the challenges of those who came forward. Both equity and equality within engineering must be sought to make the predominately male environment more inclusive.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".