Bibliographic record
Abstract
This paper argues that an essential task for managing complexity in design is clarification of key terms within the field, and most importantly the term that defines the field itself: Design. This position rests on the argument that theory—a key tool for managing complexity in design—is weakened by ambiguous terminology, and crucially, ambiguity of the word design. Although it has been well documented that design is a highly ambiguous term and that this is problematic for the field as a whole, many designers are resigned to this fact since it is unclear how one can resolve differences of opinion about what such a central and sensitive term means. This paper argues, though, that once designers have a better understanding of the process of definition—a process that has its own complexities— they might see the benefit of trying to define design and other key terms. To this end, this paper provides an overview of definition, borrowing largely from philosophy, which includes a survey of the types and methods of definition and issues related to each. It will also explore methods and criteria by which one can evaluate various competing definitions. From this survey, I propose that designers use a stipulative and pragmatic approach to definition outlined by Edward Schiappa (2003). Schiappa’s approach is useful because of these two key underlying assumptions: first, defining design (and related terms) is not a search for the record of past usage but an act to persuade others of how to use the word in the future, therefore the person defining must provide a compelling argument for why others’ usage should be modified; second, defining design is not a search for the ‘true’ or ‘real’ meaning of a word but instead a goal-oriented process and, therefore, dependent on the context and purpose of those defining the word.
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 | 0.044 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.019 | 0.024 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.015 | 0.040 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".