Bibliographic record
Abstract
Industry and academe has not accepted newer design methods (e.g.Design Science), and does not know about them.The methods that industry accepts (e.g.TQM, QFD, Taguchi, and many more) are claimed as "industry best practice", and industry wants academe to accept these methods as the height of knowledge.An explanation for this delay in accepting "foreign" results (in both directions) is needed.The circumstances are very complex and interacting.Design Science is an ordered, categorized and coordinated set of knowledge about designing (including knowledge about designers) and the objects being designed, a theory.For any use of methods based on Design Science, or any other methods, they must be adapted to problem and situation, to different kinds of product, and the peculiarities of the enterprise.Engineering designers develop their own methods, usually from explanations and practice.Only when an engineering designer meets a novel problem outside his/her immediate experience are any more formal procedures and methods needed.Such methods must usually be known in advance of the need to use them.There is always a general resistance to change from previous familiar ways.It is necessary for future engineering designers to learn methodology during their engineering education.German investigations have demonstrated the beneficial results of teaching formal design methodology.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".