An Intersubjective Analysis of Engineering Leadership Across Organizational Locations: Implications for Higher Education
Bibliographic record
Abstract
Engineering leadership education has become increasingly popular over the past decade in response to national calls for educational change. Despite the growing popularity of the movement, however, reform efforts continue to be piecemeal in their delivery, driven largely by the priorities of program leaders who established them (Graham, 2012). If we as engineering educators wish to more systematically develop leadership skills in our students, we should begin by empirically examining and defining our phenomenon of interest: engineering leadership. Our article takes up this challenge by investigating how 82 engineers in five organizationally distinct roles define leadership and how their respective insights are shaped by their diverse organizational locations. After weaving together the perspectives of engineers in industry, human resource professionals, entrepreneurs, politicians and interns, we propose a poly-vocal definition of engineering leadership and identify practical implications for engineering leadership educators.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".