Innovative outreach programs to attract and retain women in undergraduate engineering programs
Bibliographic record
Abstract
Statistics Canada figures for 1998 show that women comprise two-thirds of graduates with degrees in fine arts, humanities and social sciences, yet only 12% of graduates in the science and technology fields are women. For the engineering profession alone, the figures are even more daunting - only 5% of registered Professional Engineers in Canada in 1998 were women. We can no longer afford to exclude the vast pool of talent represented by women, therefore effective recruitment and retention programs are necessary to encourage more women to consider a career in the field of engineering. This paper describes several projects undertaken at Ryerson Polytechnic University to increase the participation of women in engineering. These projects include the 'Discover Engineering' Summer Camp, in-class high school workshop program, one day engineering career conference, on-line mentoring program, student 'drop-in' hours and an incoming student welcoming reception. The paper discusses the impact of these initiatives, as measured by follow-up surveys and other evaluation tools. Figure 2, showing data broken down by gender, reveals an interesting snapshot of the state of engineering profession in Canada (Ontario data is representative of national statistics). While reasons for declining interest in engineering as a career are a focus of intense discussion within the profession and academia and are based on larger societal issues, it is clear that more women than ever are interested in pursuing 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".