What Makes an Exemplary Engineering Leader? In the Words of Engineers
Bibliographic record
Abstract
Recent research suggests that engineers can be more inclined to identify leadership in the practices of admired colleagues than recognizing themselves as leaders [1-4]. We believe by asking engineers who they view as exemplary engineering leaders, we can sidestep some engineers’ reluctance to adopt leadership as part of their engineering profession to allow us to better understand the qualities of engineers who lead. This work is based on two survey questions that ask engineers 1) to identify exemplary engineering leaders in their lives, and 2) to describe what makes an exemplary engineering leader. While we set out to analyze the 828 open-ended responses through Engineering Leadership Orientations framework [3], our analysis of the responses revealed 3 perspectives engineers take to define exemplary engineering leadership: an individual’s values, attributes and traits, an individual’s skills, abilities, and behaviours, and lastly, an individual’s impact to community, society, or the profession. This works contributes to the developing definition of engineering leadership by providing the perspective of engineering professionals from industry.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".