Productive Frictions: Moving from Digital to Material Prototyping and Low-Volume Production for Design Research
Bibliographic record
Abstract
<p>In this paper we discuss the low-volume production of an interaction design research product known as the tilting bowl. The form of the tilting bowl was designed with 3D modeling tools and utilized digital fabrication for rapid prototyping. The final form was produced in a small number of glazed ceramic forms with embedded electronics and actuators. We focus on the lessons we learned from the challenges and design opportunities that arose in moving from digital processes to ceramic processes. We reflected on these lessons and developed thematic notions we refer to as frictions. These include shifting constraints, naïve expertise, manual automation, and dynamic materiality. The contributions of this paper are new design insights into the combination of digital and material processes for studio based prototyping and low-volume production and adds to the emerging relevance of digital fabrication, physical fabrication, and physical materials to interaction design and HCI research.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".