Functional reasoning theories: Problems and perspectives
Bibliographic record
Abstract
Functional reasoning (FR) enables people to derive and explain function of artifacts in a goal-oriented manner. FR has been studied and employed in various disciplines, including philosophy, biology, sociology, and engineering design, and enhanced by the techniques borrowed from computer science and artificial intelligence. The outcome of FR research has been applied to engineering design, planning, explanation, and learning. A typical FR system in engineering design usually incorporates representational mechanisms of function concept together with description mechanisms of state, structure, or behavior, and explanations and reasoning mechanisms to derive and explain functions. As for representation, philosophers have long argued whether function of an artifact is a genuine property of it. As for explanation and reasoning, they have produced theories for functional ascription by an external viewer as part of an explanation. To build an FR-based system, the theory based on which the system is built and the underlying assumptions must be explicitly identified. This point is not always clear in the engineering of FR-based systems. Understanding the underlying assumptions, logical formulation, and limitations of FR theories will help developers assessing their systems correctly. The purpose of this paper is to review various FR theories and their underlying assumptions and limitations. This later serves as a benchmark for comparing various FR techniques.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.007 | 0.023 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".