Bibliographic record
Abstract
Systematic engineering design can use thetools, models and methods recommend by Hubka to helpdesigners, especially in critical situations. These methodscan be applied for novel designing, or for re-designing.In teaching, observations of students revealed difficultiesin applying and formulating “internal and crossboundaryfunctions” of technical systems (TS), and of“operations” in a transformation process (TrfP).A strategy to overcome these difficulties is to introducesufficient theory, then to provide a re-design problem,using an existing commercial device, (a) as a cut-away toshow the inner workings, and (b) as a complete devicethat can be dis-assembled – accompanied by engineeringdrawings of each part, an assembly drawing and anexploded view. Students (1) studied the hardware anddrawings, (2) identified elemental organs, and usefulorgan groups, and (3) wrote their interpretation of whateach organ group is capable of doing – the TS-internaland/or cross-boundary functions, to be represented in aTS-function structure. An example is offered.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".