Designing and life cycle engineering—a systematic approach to designing
Bibliographic record
Abstract
Engineering a system for its whole life cycle involves the phase of designing the system, when all requirements for function, capabilities, life cycle and societal (economic, ergonomic, aesthetic, law conformance, etc.) properties should be established, and as far as possible fulfilled. Designing need not be just an intuitive and idiosyncratic procedure—it can be made more rational. Design Science has set itself the goal of providing a comprehensive theory. Theories cannot be directly applied. Methods and models derived from the theory can be applied if the relevant parts of the theory are understood (at least at an awareness level) by the applying designer, and the actions and documentation are demanded by design management. The resulting improvement in the process will also render the design process more transparent, and open to review and audit, as demanded by the ISO 9000 series of quality standards. Some contacts with the disciplines of systems engineering and life cycle engineering are indicated. Most formal models of design processes are formulated for novel design problems. They can help to clarify the problem in a more formalized way. Design Science can effectively be applied to novel or redesign problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".