Gender Differences in the Early Career Experiences of Engineers in Canada
Bibliographic record
Abstract
Canada has an urgent need for more engineers to support its infrastructure, advance technology, and solve increasingly complex human, economic, and environmental problems. Women have often been identified as a resource who can provide new perspectives, solutions, and innovations. While women’s participation in engineering programs has increased over the last 50 years, their participation rate in the workforce has not, keeping engineering as a male-dominated occupation. Despite challenges, women graduates have entered the engineering workforce, but often they have not stayed. The purpose of this quantitative study is to explore the early career experiences of engineering graduates to identify patterns shaped by the graduates’ gender. Applying feminist lenses to the most recent data on Canadian graduates available at Statistics Canada and utilizing advanced quantitative methods, we study BEng graduates from Canadian universities. This study provides a broader understanding of the phenomenon of women’s underrepresentation in engineering and presents findings that can help retain more women in the occupation. Three samples of BEng graduates with over of 10,100 participants were included in this study to answer three main research questions: a) are there gender differences in the duration of job search and types of jobs these graduates obtained after graduation?; b) are there gender differences in job satisfaction among young engineers?; c) are there gender differences in the intention to look for another job once in a first engineering job? Themes and subthemes relevant to women’s underrepresentation in the occupation are found to help answer these questions. Recommendations for policy and future research are discussed.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".