The Formal Modeling Of Engineering Design Information By Means Of An Axiomatic System
Bibliographic record
Abstract
There is mounting evidence in the current literature which suggests that our collective understanding of engineering design is insufficient to support the continued growth of the engineering endeavor. Design theory is the emergent research field that addresses this problem by seeking to improve our understanding of, and thus our ability to, design. The goal of this author's work is to demonstrate that formal techniques of logic can improve our understanding of design. Specifically, a formal system called the Hybrid Model (HM) is presented; this system is a set-theoretic description of engineering design information that is valid independent of (a) the processes that generate or manipulate the information and (b) the role of the human designer. Because of this, HM is universally applicable to the representation of design-specific information throughout all aspects of the engineering enterprise. The fundamental unit in HM is a design entity, which is defined as a unit of information relevant to a design task. The axioms of HM define the structure of design entities and the explicit means by which they may be rationally organized. HM provides (a) a basis for building taxonomies of design entities, (b) a generalized approach for making statements about design entities independent of how the entities are generated or used, and (c) a formal syntactic notation for the standardization of design entity specification. Furthermore, HM is used as the foundation of DESIGNER, an extension to the Scheme programming language, providing a prototype-based object-oriented system for the static modeling of design information. Objects in the DESIGNER language satisfy the axioms of HM while providing convenient programming mechanisms to increase usability and efficiency. Several design-specific examples demonstrate the applicability of DESIGNER, and thus of HM as well, to the accurate representation of design information.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".