An Intersubjective Analysis of Engineering Leadership Across Organizational Locations: Implications for Higher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. 
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it