The Development of a DfX
Bibliographic record
Abstract
Abstract The Development of a DfXThis paper uses a historical case study to develop an understanding of how DfXs may develop.This paper begins by outlining the development of engineering design guidelines, also known as“Design For X” or “DfX”. DfXs are guidelines that engineers may use to better the outcome oftheir design process with respect to the X in question. For example, Design for Safety is acommon DfX, used to ensure an engineer’s final output minimizes the occurrence of harm tousers. The more recent case study of the Design for the Environment (DfE) provides a possiblemodel for the emergence of a DfX.Historical records for the development of DfE show a discernible pattern which can beunderstood in five basics elements that occurred in the formation of DfE guidelines. Theelements include: (1) Catalyst for Change – A Push, (2) Isolated Cases and Examples – A BraveStep Forward, (3) Developing Heuristics – A Simple Set of Rules, (4) Developing a Process – ARecipe for the Implementation of the DfX, and (5) Metrics – A Measure of Success. Separatefrom the core 5 elements, we recognized a sixth outlying element that can occur as the first orlast step in the development of a DfX. We have termed this sixth element Codes and Standards –A Push or a Pull on the DfX. Abstracting from these elements, we have generated a model thatcould apply to the development of any DfX.We hypothesize that understanding how a DfX emerges has two main values: (1) Identifyingemerging DfXs could provide corporations with a competitive edge. (2) Identifying emergingDfXs could allow engineering design researchers to better target their work. A historical exampleof a corporation that recognized, and then adopted, DfE in its early stages highlights theadvantages conferred to this corporation due to its early adoption of DfE. Similarly, byunderstanding where a DfX lies in its development, the design researcher may target their nextwork towards the cutting edge of the field.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".