When Engineers Become Managers: Learning from Current Engineering Managers to Advance Engineering Management Education
Bibliographic record
Abstract
This paper reports on the findings of a combined qualitative and quantitative research case study into engineering management (EM) practices in offshore Oil and Gas industry in St. John's, Newfoundland. It was designed to examine the management challenges faced by EMs in this sector, how EMs are selected, how engineers respond to being made managers and to being expected to lead, and what training and development is needed to be an engineering manager. Specifically, the study investigated the relationship between each of twenty three (23) identified EM job activities on (i) difficulty, (ii) frequency of problem occurence, (iii) benefit of training, and (iv) importance of job success. The data indicated that among the 26 EMs surveyed, problems occured most often and caused the most difficulty in the activities considered most important to success, specifically interpersonal communication, people management, leadership, motivating, finance and projects. The top training needs were identified as project management, effective speaking, motivating, leadership, decision making techniques, risk analysis, personal efficienty, and effective writing.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".