Preliminary Employment Trajectory Findings from a Pan-Canadian Survey on Career Motivations and Aspirations of Undergraduate Engineering Students
Bibliographic record
Abstract
Engineering students have varied vocational motivations and aspirations. Clarity regarding these factors, how they can be nurtured, and how they can impact attrition from the profession can help foster retention and diversification. A survey was deployed in 2023 at six Canadian schools, garnering about 2500 responses. Approximately 45 questions examined demographics, motivations for choosing engineering, engineering identity, personality characteristics, career aspirations, and influencing factors. We focus on three research questions in this study: 1) to what extent do students self-identify as engineers (in training), 2) do students have preferences concerning the types of organizations they aspire to work within, and 3) do students have preferences concerning the types of work (or employment trajectories) they aspire to pursue. Responses were examined across gender, year of study, and institution, and were characterized using descriptive statistics, significance testing, and ANOVAs. Initial results provide a rich picture of student career interests, varying by demographics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".