An axiomatic theory of engineering design information
Bibliographic record
Abstract
Recent research in design theory has sought to formalize the engineering design process without particular concern for the paradigm used to model design information. The authors propose that no correct formalization of the design process can be achieved without first formalizing the semantics of the information used in the process. To this end, the authors present a new formal theory of design information. The theory, called the Hybrid Model, is an extended form of axiomatic set theory, and relies on it for consistency and logical rigor. The theory is stated as a collection of axioms, using a standard logic notation. Design entities are modeled by formal units called objects. Generalized functions and relations are used to formalize important ordering schemes and abstraction mechanisms relevant to design, including classification by structure and by function, aggregation, specialization and generalization. The hybrid model is meant not only to aid in the study of the design process itself, but also to improve communications between designers, assist standardization of design specifications, and develop new, powerful software tools to aid the designer in his work.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".