WHAT WOMEN STUDENTS WANT THEIR INSTITUTIONS TO DO TO MAKE ENGINEERING EDUCATION MORE INCLUSIVE AND LESS “CHILLY”
Bibliographic record
Abstract
It has been over 30 years since science and engineering classrooms were first described as “chilly” environments for women. Since then many engineering programs in Ontario have worked to diversify their student populations with a particular focus on recruiting more women into engineering education. Despite the increase in the number of women enrolled in engineering education, incidents of sexism and microaggressions based on sexual orientation and race continue to be experienced by women in these programs. In many engineering faculties in Ontario work on equity, diversity and inclusion is still fairly new. Members of the community who are marginalized need to be consulted in order for this work to be impactful on their experience. This paper uses an intersectional framework to present preliminary results on what women studying in undergraduate engineering programs believe their universities could do to make engineering education more inclusive. This paper argues that women students in engineering are still in search of a community. Our women students want more representation of women in engineering, further education and awareness of equity issues, acknowledgement of their experiences and opportunities to connect with other women in engineering.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".