Counting Past Two: Engineers' Leadership Learning Trajectories
Bibliographic record
Abstract
Abstract Abstract: In the early 1950s, many science and technology focused organizations in the United States and Canada began to formalize a technical career track to accommodate the professional aspirations of engineers reluctant to abandon technical work for management [1-7]. While the resulting dual career track model—characterized by both managerial and technical ladders—remains dominant in human resource management theory, there is little evidence that engineers’ actual work experiences map on to two discrete domains [8, 9]. Our paper expands the dual track model by tracing the actual career paths and leadership learning experiences of 28 senior engineers in eight industries. We do this, not to better understand engineers’ career paths for their own sake, but rather to examine how engineers learn to lead in workplace contexts. In particular, we ask two organizationally related research questions: 1) What career paths do engineering leaders follow? and 2) How do they learn to lead along the way? After briefly reviewing the literature on engineering leadership development and engineers’ career paths, we introduce the situated learning perspective that grounds our work and present our findings in two parts. Part one characterizes six discrete paths—1) Company man, 2) Technical specialist, 3) Boundary spanner, 4) Entrepreneur, 5) Social impact change agent, and 6) Invisible engineer, and part two identifies salient leadership learning experiences that correspond with each path. We conclude by discussing the implications of our findings 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".