A vision for advancing systems science as a foundation for the systems engineering and systems practice of the future
Bibliographic record
Abstract
Abstract In this paper, I argue that the fragmented state and uneven maturity of current systems science will render it increasingly inadequate for meeting the future needs of the engineering and practice disciplines depending on it. I explain that it is not the case that System Science is a holistic discipline in contrast with the reductionism of classical science, but that Systems Science has both reductionistic and holistic dimensions, dealt with respectively by two “movements” within systems science, which I will designate as “Complexity Science” and “Systems Research”. I argue that in many situations the internal workings of a system can be satisfactorily addressed with the mainly reductionistic methods of Complexity Science, whereas when external factors play a significant role, the mainly holistic methods of Systems Research are brought to the fore. This suggest that Complexity Science and Systems Research are not really as disjunct as often portrayed, but represent special cases under a wider conception that would hold across a spectrum of ratios between ‘internal complexity’ and ‘external complexity’ of the system of interest, and that would entail a differential emphasis on reductionistic and holistic methods based on contextual factors. Such a wider conception could not only help to unify systems science, but would also support analysis and intervention in the ‘middle ground’ between these polar types. This is relevant for Systems Engineering and Practice because as the world's complexity grows engineers and practitioners will increasingly have to deal with situations that are complex both internally and externally. This suggests an increasingly urgent need for the development of the envisioned ‘wider conception’ of systems in which we can deal in an elegant and principled way with shifts in the balance between internal and external complexity. In this paper I propose that a scientific general theory of systems could provide such a wider conception, and that it could serve as a basis for the unification of systems science, provide support for the scientific maturation of the discipline, and extend the capability and utility of systems science in important ways. I present approaches and frameworks that would support the development of such a theory, present wide‐ranging evidence suggesting that we are in a favourable position for developing one, and indicate important areas to focus on in future research.
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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 | 0.065 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.059 |
| Scholarly communication | 0.027 | 0.035 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".