A THEORETICAL FRAMEWORK FOR AN INTELLIGENT DESIGN CATALOGUE
Bibliographic record
Abstract
This paper outlines continuing work on the intelligent design catalogue. The intelligent design catalogue seeks to create a virtual design environment that is linked to a catalogue of standard parts. The theoretical framework for this research draws on several engineering areas. Within manufacturing, process plans can be developed in a virtual environment independently of the machines on the shop floor just as products can be conceptually designed independently of the standard parts available. The standard parts themselves can be grouped borrowing from classification schemes of Group Technology. Object-Oriented Programming (OOP) provides an environment for the development of the software that runs the intelligent design catalogue. As the objects of OOP parallel standard components, OOP also serves as a design paradigm after which the catalogue can be modelled. . Design theory suggests frameworks for developing a (semi-) hierarchical structure for cataloguing parts and design case studies offer insight into differences between novice and expert designers.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".