Are Hardhats Required for Engineering Identity Construction? Gendered and Racialized Patterns in Canadian Engineering Graduates’ Professional Identities
Bibliographic record
Abstract
Despite ongoing efforts to increase diversity in engineering, women continue to be underrepresented in the field, making up only 15% of licensed professional engineers in Canada [1].This persistent underrepresentation has been explained in part by the challenges women and other underrepresented groups face in identifying with engineering, including feeling inauthentic in traditional engineering roles, and doing additional work to manage impressions and demonstrate professional fit [2][3][4].Studies on engineers' career paths have also shown that underrepresented groups in engineering are more likely to be streamed into non-traditional career pathways with less social capital, negatively impacting their identification with the field [5][6][7][8].As identification with the profession can predict the persistence of both engineering students and professionals [9], there is a need to understand factors that influence engineering identity, and how these factors may vary by demographics.Using data from a 2022 national survey of engineering graduates (n=982), we examine the engineering intensity of participants' professional identities disaggregated by gender and race.Our findings reveal that role type, technical focus, and application of background education were salient themes across the entire sample, reflecting the prioritization of traditional and technically oriented work in engineering culture [10].For engineering educators, understanding the factors that influence engineering identity has implications for their ability to foster their students' sense of belonging, encourage their retention in the field, and improve their access to a range of meaningful engineering career paths.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".