Complex systems management competency for technology modernization
Bibliographic record
Abstract
Industrial technology modernization requires solutions to the problems of the complex systems evolution.Entities involved in the process of modernization lack unified centralized control and act as standalone businesses, while stakeholders in the processes pursue their own, all too often conflicting interests.Having to operate amid severe external uncertainty, these standalone businesses act as isolated agents, with their make-up changing at different periods, yet the success of technology modernization depends on their coordinated action.It is critical for the success of the system evolution to have enough highly qualified personnel with expertise and competencies, engineering and economic ones in the first place, that match the complexity of the systems being managed.The article analyzes the tasks that pop up throughout the course of modernization.The analysis is used as a basis for defining engineering and economic competencies and for substantiating their significance as a key resource of the industrial systems of the future.This assumption finds a confirmation in a summary of appropriate global trends done by the authors.Systems engineering is used as a case of the application of the competencies as part of methodologies that were created in response to challenges associated with the growing complexity of technological and organizational systems.The study also gives examples of the need for engineering and economic competencies arising in the course of technology modernization in the electric power industry.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".