Bibliographic record
Abstract
There is one thing that is certain in design: design is uncertain.The uncertainty in design is rooted in the nature of the design problem: the recursive evolution of design problem along with design solutions and related design knowledge (Zeng, 2004;Zeng & Cheng, 1991;Zeng & Gu, 1999).Design uncertainty takes many forms; one most acknowledged form is its unpredictability in that the future decisions are uncertain in its behaviour, in its value, and in its reliability; another uncertainty comes from misunderstanding in design communications in that people may have different perceptions for the same received information (Nguyen & Zeng, 2017;Tan et al., 2016); the third one is resulted from the imperfect understanding of a design situation.This issue presents three research papers addressing different aspects of design uncertainty.The first paper, titled "Classification of Change-Related Ilities Based on a Literature Review of Engineering Changes" by Colombo et al., argues that uncertainty is a major hurdle for effective and efficient system changes, which are central to the design of complex and innovative systems.A meta-framework is proposed to define and identify the needed Ilities that may facilitate changes in technical systems.Ilities represent a common terminology to reduce the ambiguity in effective communication of knowledge resided in best practices.Seven engineering change features are summarized based on nine change Ilities frameworks and about 330 sources.Founded on the systematic literature review of change-related Ilities, the authors aim to propose a classification of the Ilities in terms of system lifecycle.Some future directions are also discussed in the paper.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".