Bibliographic record
Abstract
Following papers by this author in recent CDEN conferences, the concept of a complete theory-based classification is presented for the properties of existing transformation processes TrfP, and of their existing driving technical systems TS. Requirements for future TrfP and TS must include requirements set by the designing and manufacturing organization(s), the first three life-cycle phases in the theory-based model. An engineering design process intends to translate these requirements in several stages to the desired properties of TrfP and TS, using conscious or sub-conscious procedures for creative and routine steps. For novel systems, this translation progresses via models of structures of TrfP, technologies, TS-internal and cross-boundary functions, organs and construc-tional parts. For redesign, mainly the more concrete structures are useful. Superimposed on this progress is a frequent cycle of problem-solving, including search for alternative solution proposals. Recent insights have shown that the requirements can be iteratively translated into properties. The difference between achieved (anticipated) properties and the relevant requirements dictated the necessary iteration behaviour, and drive the process of establishing the final proposed solution. Such a formalized experience of staged and iterative designing is a good methodical basis for novices, and for design applications in which the process must substantially depart from routine procedures. Some case studies are referenced to demonstrate the application of this systematic and methodical approach.
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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".