Not Just for Undergraduates: Examining a University Narrative-Based Career Management Course for Engineering Graduate Students
Bibliographic record
Abstract
The experiences of graduate engineering students enrolled in a credit-bearing, career management course at a Canadian University were explored from a narrative perspective. Scant literature exists on the outcomes of career planning courses at the graduate level, largely because these classes tend to be aimed at undergraduate students. Individual interviews were conducted with 10 students who completed the semester-length course. The inquiry focused on students’ life-career plans and their experiences in the course. Applying social constructivism to career development as a theoretical framework, thematic analysis was used to generate results in the form of three main thematic categories. These categories included: fostering career awareness and exploration skills; finding affiliation with others; and developing optimism and confidence. Findings highlight the benefits for graduate students who are offered opportunities to develop career planning skills through credit bearing courses. Implications for practice, policy, and research exist based on the data analysis, including alternative strategies to incorporate life-career planning skills into graduate-level coursework.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".