A holistic approach to supporting women and girls at all stages of engineering education.
Bibliographic record
Abstract
In 2010 enrollment of women in Canadian engineering undergraduate programs was 17.7%. Although this reflects a slight increase in the number of female engineers since 2005, much effort is needed to increase diversity in the profession by fostering interest in, recruiting and supporting this group. The following outlines the holistic methods used by the University of Waterloo’s Women in Engineering (WiE) group to support females in all stages of their educations (Kindergarten to Post Graduate). Early Engagement of girls in grade K-8 is used to foster interest in STEM (science, technology, engineering and math) and inspire them to enroll and succeed in these courses in grade 11 and 12. Programing for high-school students are designed to highlight how individual interests can be paired with many engineering fields and can lead to a fulfilling professional career. Many WiE programs for elementary and secondary girls are delivered in “girls only” environments to reduce some of the social pressures that may play a role in discouraging them to pursue STEM interests. To further this, the location of each programs is chosen to suit the audience by providing a safe, accessible and comfortable environment, either on campus or within the girls’ communities. Once young women choose engineering, the WiE group offers opportunities for support at the University of Waterloo. These opportunities range from networking with classmates and alumni to mentoring and professional development. These programs are organized both at a faculty and staff level as well as through a student driven platform. Through engagement and support for female students over the continuum of education WiE helps to inspire more women to pursue and enjoy careers in engineering, contribute to diversity of ideas within the profession and create role models for future engineering students.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".