Bibliographic record
Abstract
Experiential Knowledge Special Interest Group (EKSIG) focusses on the understanding of ‘knowledge’ and ‘contribution to knowledge’ in design research, especially in the areas where designing forms part of the research process. The EKSIG strand at DRS2024 takes a closer look at the new and changing materiality of design practice that we, designers, face, due to digitalization and its challenges and benefits. Several areas of design practice and research involve processes of making things. More often such processes unfold in a hybrid form combining both making by hand and with tools, both analogue and digital. This year’s EKSIG strand focusses on discussing the theme ‘Making in the Digital Era’ that illuminates designers’ insider perspectives on making and embodied experience in hybrid analogue and digital material processes. The blurry border between the two modalities enables the designers to delve themselves into the hybrid environment of making in which they can move seamlessly between the analogue and the digital – but what happens with the experiential knowledge of materials in this process? Being insiders in such processes, designers can provide insights into their direct embodied experience in hybrid processes and contribute to the theoretical discussion of ways of knowing and how they use their experiential knowledge in this transition from the analogue to the digital realm – and back. The EKSIG strand provides a forum for discussing the concept of ‘thinking in making’ in design research that entails action and perception coupling, which results in artifacts as extensions of the designer-researcher’s experience.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".