Illustrating Business Relevance of Systems Engineering via Storytelling
Bibliographic record
Abstract
Abstract Many software‐centric organizations do not embrace Systems Engineering, whether lacking awareness or perceiving it too onerous for value delivered. While some embrace Digital Engineering, Design, and Innovation to advance their market position, some continue to experience sub‐optimal results and downstream consequences. These realities show Systems Engineering becoming inconsequential in today's business settings, to the detriment of success and customer value. Effective storytelling, showcasing situational implementation of the right elements of Systems Engineering, can address this. Stories engage, inspire, and create connection. When layered with multiple dimensions and meaning, they become timeless. This paper describes four vignettes about enhancing success in software‐intensive enterprises by leveraging Systems Engineering in context, flexibly and fit for use, and compatibly with other disciplines. The vignettes illustrate how Systems Engineering strengthens performance and positions for flexibility, adaptability, and resilience in a fast‐changing, complex world. The paper concludes with implications for Systems Engineering outreach, leadership, and influence.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".