Prototyping across the Disciplines
Bibliographic record
Abstract
If people from different fields are going to work together on projects, then they need to begin to understand each other. They can be separated by the words they use, the ways they work and how they think. However, in many fields there is common ground, in the attempts to create what is sometimes called inventive knowledge. These fields progress not only by understanding increasingly more about what already exists, but by making guesses about possible better futures. The guesses consist of small forays into that future, using strategies that are variously called learning through making, research through design or, more simply, prototyping. While traditionally associated primarily with industrial design, and more recently with software development, prototyping is now used as an important tool in areas ranging from materials engineering to landscape architecture to the digital humanities. This book collects current theories and methods of prototyping in a dozen disciplines, illustrating them through case studies of actual projects, whether in industry or the classroom. This edited collection aims to provide a context, a theoretical framework and a set of methodologies for interdisciplinary collaboration in design. Each chapter offers a different disciplinary perspective on prototyping, providing a case study as a point of comparison for identifying commonalities and divergences in current practices. Contributions are from a group of scholars with worldwide experience of working and presenting in design, and who are currently based in Canada, the United States, Chile and Brazil. This book isn’t just about design across the disciplines, it is about how prototyping works in different disciplines. Prototyping is a crucial part of the design process, and a practice used by creators from all design disciplines, from architects and engineers, to industrial and service designers, to test a concept or process and evaluate an idea. Much research has been published on prototyping in design; what makes this new book unique is the cross disciplinary nature, showing designers how they can learn from various approaches to improve their skills. Disciplines discussed include post-human design, theatre, tabletop game design, landscape architecture and arts entrepreneurship. Primarily of interest to design scholars and practitioners with an interest in integrative design. Undergraduates and graduate students in design, HCI (human-computer interaction) and the digital humanities. Textbook potential.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".