WHERE ARE THEY NOW? UNDERSTANDING CAREER PERSISTENCE OF WOMEN IN ENGINEERING PRACTICE IN MANITOBA
Bibliographic record
Abstract
Women are underrepresented in the Canadian engineering profession at a rate of nearly 10:1 when compared to their male counterparts. This poses a problem for a profession whose goal is to provide innovative and inclusive solutions that work for all people. In Manitoba, while women are underrepresented in engineering at the same rate as the national average, there is also evidence that some either never enter the profession after graduation, or leave at various stages in their career. While there is significant literature to examine recruitment to and retention of women to engineering study at colleges and universities, there is much less research on women’s persistence in engineering practice post-graduation. Accordingly, this study was designed to address the research question: What are the elements of women’s experiences in the engineering profession that both enable and deter their persistence in the profession, and how do these elements or factors interact with one another over time & space? The findings generally align with the literature-based conceptual framework and indicate that this is a multi-dimensional problem that includes factors such as a need for improved work-life balance, workplace cultural shifts, and confidence building. Implications of these finding include a need to support both new graduates in the licensure process and former members who wish to return to practice. Concrete proposals to ease re-entry to practice are also presented.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".