Envisioning Future Systems Engineering Principles Through a Transdisciplinary Lens
Bibliographic record
Abstract
Abstract The purpose of this paper is to examine systems engineering principles from a transdisciplinary perspective. The motivation for this investigation was the question “What might change in the principles captured by the INCOSE Principles team (Watson, et al, 2019) as transdisciplinary systems engineering becomes the norm?” The revised INCOSE definition of systems engineering (INCOSE 2019) recognizes transdisciplinarity as an essential part of systems engineering going forward. Looking back through millennia of engineering, it is possible to identify aspects of the practice of engineering systems which used some transdisciplinary approaches, but only recently have transdisciplinary systems engineering practices been systematically characterized and described in depth. This paper identifies areas in the understanding of systems engineering principles which can be revised to reflect transdisciplinary systems engineering more effectively. Hopefully, this will help engineering professionals implement systems engineering principles to more effectively engineer systems to meet the needs of stakeholders more cost‐effectively, with less conflict among stakeholders.
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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 | 0.024 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".