Merit for Mobility: A Mixed-Methods Study of How Canadian Engineers Understand Success and Social Structures.
Bibliographic record
Abstract
While stark racial and gender inequities continue to plague the engineering profession, addressing systemic inequities remains challenging for the profession given members are socialized to think of themselves as free agents, unencumbered by social structures. My thesis offers a mixed-methods analysis to the prevalence of agentic and structural explanations of career mobility among 952 Canadian engineering graduates who responded to a 2022 national engineering career path survey. Theoretically rooted in the works of Cech (2013a, 2013b) on cultural hegemony within engineering (rooted in Gramsci, 1971), and the works of Bourdieu (1993) and Giroux (1983) on cultural reproduction, my thesis explored four research questions: (1) to what extent do engineering graduates recognize the role of social location on their careers? (2) How does graduates recognition of the role of social location break down by gender and race? (3) How do graduates explain the relationship between social location and career mobility? (4) How do graduates explanations reify or resist dominant agentic ideologies of success? My findings show that individuals from under-represented groups in the engineering profession are more inclined to view their social location as a non-neutral feature of their career mobility, and that (a) engineering culture and (b) disproportional access to supports are key factors impacting engineers’ career mobility dependent on social location. This work exposes patterns of professional buy-in to agentic, meritocratic norms in engineering culture. When we name dominant ideologies without illustrating how they land in the lives of engineering graduates, we risk further disadvantaging those who are negatively impacted by structural inequities.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".