Domain-Independent Design Theory and Methodology to Boost the Adoption of Design Methods
Bibliographic record
Abstract
Since its inception, research on design methods has encompassed a number of objectives and fields. In this fragmentary and evolving landscape, a reflection was apparently needed on the chance to provide a general framework and understand the progress (if any) towards a unified and domain-independent design theory and methodology. The issue was discussed by the authors, among the others, during a workshop organized by the Society of Design and Process Science. The paper reports the most important points that emerged in the debate, which was kicked off by panellists’ talks providing different perspectives on domain-independent design and the adoption of design methods by industry and practitioners. The discussion highlighted the relevance of design education, individual factors, as well as the role of designers in nowadays’ changing world. A major take-away from the workshop is the forecast that, in the foreseeable future, a shared design knowledge will be developed, but this will be juxtaposed by peculiar and bespoke design practices.
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 | 0.229 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".