Bibliographic record
Abstract
As the frontiers of technology advance and the work of engineers takes on an increasingly important role in our economy, companies with effective product development and engineering processes will be poised to create value for their shareholders. Those without the will to improve engineering and product development processes will be destined to lag behind. Our university engineering programs focus on graduating technically sound engineers. Students study the disciplines of structural design or fluid mechanics. However, in both North America and Europe, little attention is paid to teaching the practice of engineering management. Engineering programs typically contain a fourth-year course on engineering economics, where students are taught the mechanics of discounted cash flows and budgets. The courses do not deal with the challenges of managing complex engineering-driven companies. With this gap in the training of engineers, it should come as no surprise when a graduate engineer practices engineering for two or three years and then leaves the profession to take an MBA. Many of these bright young engineers cut all ties to engineering. However, MBA programs are not designed to create engineering managers. The best of them teach the integration of management disciplines to teach general management; however, the worst provide the engineer with little more than a few specialized tools to apply in the area of marketing or finance. Generally speaking, the practice of engineering management is not taught in our universities.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.494 | 0.317 |
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".