Enhancing Technology Development Through Lifelong Education Of Engineers And Technologists As Creative Professionals
Bibliographic record
Abstract
There is growing recognition worldwide that traditional graduate engineering education neither fits the engineering innovation process necessary for competitiveness in the global economy nor reflects the way that graduate engineers and technologists learn and develop as professionals, innovators, entrepreneurs and leaders in industry.In today's global economy, engineering innovation is recognized as a continuous, systematic needs-driven process, which is highly dependent upon the provision for lifelong learning, growth, and development of the nation's graduate engineers and technologists in industry beyond their entry-level undergraduate baccalaureate preparation.Because of profound changes in engineering practice for real-world innovation, a transformation is underway in the U.S. Science and Engineering (S&E) innovation system.A concurrent, nonlinear model of needs-driven systematic engineering innovation, which is supported by directed scientific research, is replacing the sequential, linear research-driven model of engineering innovation.Graduate education must be responsive to this change and must build a new type model of in-service graduate professional education which reflects the substantial changes and characteristics of the engineering innovation process itself, and the stages of lifelong growth, professional dimensions, and leadership responsibilities associated with the modern practice of creative engineering in a knowledge-based, innovation-driven economy.Whereas traditional research-based graduate engineering education and teaching have resulted during the last three decades as a byproduct of the linear research-driven model of innovation, a new model of graduate professional education has been developed which focuses on lifelong professional education for emerging and experienced engineering leaders in industry as creative problem-solvers, technical program makers, technology policy makers, and leaders in the modern context of engineering practice for creative technology development and innovation.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.015 |
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".