Ontario Network of Women in Engineering Case Study: Indicators of Success and Reflections on Lessons Learned
Bibliographic record
Abstract
The Ontario Network of Women in Engineering (ONWiE) was formed in 2005 and is a partnership of the 16 Schools and Faculties of Engineering and Applied Science in Ontario—a group that accounts for almost half (44%) of undergraduate engineering students in Canada. The mandate of the network is to advance gender diversity in the engineering profession by encouraging the next generation of women to study and pursue careers in engineering. By sharing resources and effective outreach practices among members, the collective impact of ONWiE has been significant. Since its formation, ONWiE programs have influenced young women, their parents, and community leaders, thus far engaging with over 28,000 participants. Both qualitative and quantitative indicators confirm ONWiE’s efficacy in dispelling stereotypical ideas regarding who can be an engineer, what engineers do, and the globally important role they play. Since 2005, the number of female students applying for engineering programs in Ontario has tripled. This case study highlights key successes, not only in terms of immediate feedback from participants in ONWiE programs, but also its longer-term impacts on gender diversity within undergraduate engineering programs. We also reflect on the lessons we have learned—not least, the factors that have contributed to the success of the collaboration—and the value of linking outreach programs to social science research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".