Making the path to engineering leadership more equitable: illuminating the (gendered) supports to leadership
Bibliographic record
Abstract
There is an assumption of meritocracy in engineering that belies the interpersonal and institutional supports that contribute to professional outcomes. In a qualitative study involving career history interviews and using social support theories as a framing device, we explored the supports that contributed to the development and practice of engineering leadership for 29 Canadian engineering leaders working across different industry sectors. Our findings suggest that leaders were consistently supported through sponsorship, constructive appraisal, a learning workplace culture, and the care work of family, peers, and others. Consistent with the literature on professional development, we found a disparity between genders in the way engineering leaders were supported, from the level of sponsorship to experiences of negative organisational culture and the way gendered family norms affected leadership advancement opportunities. Drawing from our findings, we present lessons for engineering leadership educators, including the need to centre equity in leadership education. We do this in part to prepare students for the challenges and inequities within current workplace realities, but also to equip them with the knowledge and skills to contribute to more equitable practices and channels towards engineering leadership.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".