A Narrative Study of Women Engineering Students' Career Development through Work-Integrated Learning
Bibliographic record
Abstract
Although work-integrated learning (WIL) is widely considered to provide academic and career benefits to Canadian engineering students, little research has qualitatively explored how students experience the engineering workplace, particularly through an equity, diversity, and inclusion lens. This thesis uses narrative inquiry to tell stories of 8 women engineering students’ experiences in work-integrated learning and how these experiences influence their broader career development. A conceptual framework is constructed, resulting in the LIRNING model for characterizing WIL placements, situated within the Life-span, Life-space theory of career development. Three distinct career narratives of Diverging, Converging, and Establishing are presented and analyzed. Findings generally support the conceptualization of WIL primarily as exploration but caveat that this exploration may not be in the same direction as students’ coursework. Gender dynamics were found to play a role in shaping the workplace experience, despite students’ hesitance to name them as harmful.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".