Ensuring resilience and agility of complex organizational-technical systems
Bibliographic record
Abstract
Modern organizational and technical systems have been developing in an environment that is marked by capriciousness, uncertainty, risk, variability, and evolution (CURVE factors).As organizational-technical systems grow bigger, their internal complexity increases, too, both structurally and dynamically.The article substantiates the appropriateness of employing the principles of systems engineering for managing such systems.The authors analyzed various theoretical concepts of and practice-based approaches to the development of systems engineering in the context of ensuring the resilience and agility of complex organizational-technical systems.Using the case of power engineering and hi-tech industries, the authors show that for organizations that operate critical infrastructure facilities it is essential to make sure that the system stays functional in adverse conditions and is able to recover quickly after a failure.It is demonstrated that for addressing the above task it is critical to use instruments that nurture interdisciplinary competences in individual professionals and in teams that manage the development of complex systems and implement major innovation projects.As part of the study, the authors also look at the possibility of using the principles of resilient systems design and the fundamental principles for agile systems engineering when managing critical infrastructure facilities.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".