Understanding the Influences of the Undergraduate Student Experience in Canada on the Career Pathways for Black Engineering Graduates
Bibliographic record
Abstract
Career pathways for engineers have shifted over the years, yet limited research in Canada has explored how these pathways differ across demographic groups, particularly for Black engineering graduates. Given the continued underrepresentation of Black professionals in STEM, this research aims to investigate the career pathways taken by Black graduates of Canadian engineering programs and the undergraduate experiences that influence careers.Guided by Social Cognitive Career Theory (SCCT), this study explores how undergraduate experiences influence career pathways. Three research questions are addressed: (1) What are the career pathways taken by graduates of Canadian engineering undergraduate programs and do these career pathways differ for Black engineering graduates? (2) Which aspects of the undergraduate student experience in Canada are most influential to career path decisions for engineering graduates and do these aspects differ for Black engineering graduates? (3) What are the experiences that Black engineering undergraduate students in Canada are exposed to that influence their career paths? The study used a survey based on PEARS (Pathways of Engineering Alumni Research Survey), and career categorization through LinkedIn profiles. A total of 289 graduates participated, including 115 who self-identified as Black. Seventeen Black graduates also participated in follow-up interviews. Findings showed statistically significant differences in career pathways. Black engineering graduates were more likely to pursue "Boundary Spanner" careers, where they work in roles adjacent to engineering such as project management, consulting, and business analysis. Differences were also observed in influential undergraduate experiences. Black graduates were significantly more likely to identify leadership in student clubs and participation in technical team projects as key influences, while being less likely to be influenced by undergraduate research. Interviews further highlighted that student clubs fostered a sense of belonging, enhanced engineering identity, and supported skill development and networking. Technical team projects allowed for both skills growth and personal interest exploration. This research addresses a gap in Canadian literature by examining engineering career pathways through a race-based perspective. The findings offer insights for educational institutions aiming to better support Black and other underrepresented students by fostering inclusive environments and providing access to undergraduate experiences that shape diverse career pathways.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".