Work-In-Progress: Understanding “Engineering Leadership” within Engineering Consulting Firms
Bibliographic record
Abstract
This paper examines how engineering leadership is understood and recognized within the specific context of engineering consulting.Engineering consulting has consistently grown over the last couple of decades in both the United States and globally.Additionally, engineering consulting is a type of Professional Service Firm (PSF), which is recognised to have organizationally distinct characteristics differing from traditional, hierarchical bureaucratic firms.These unique characteristics have implications for leadership.In this paper, we examine engineering leadership within engineering consulting through a qualitative case study on one mid-size North American engineering consulting firm.Preliminary findings from a subset of our interviews with engineering consultants across various career stages are presented.This work aligns with ASEE LEAD division's strategic initiative "Explore" as it contributes to understanding how engineering leadership is understood in professional practice.This work is also particularly relevant to knowledge-intensive, high-autonomy work environments.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".