VISMOCK: A Programmable Smocking Technique for Creating Interactive Data Physicalization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data physicalization is a research area that explores representing data attributes through manipulating the geometric and physical properties of tangible objects. We introduce VISMOCK, a data physicalization approach that leverages a fabric manipulation technique called “smocking”. VISMOCK supports the creation of interactive and dynamic data physi-calizations by extending the smocking technique with programmable components such as thermochromic pigments and shape memory alloys. Using a research-through-design methodology, we develop an initial design space for VISMOCK that shows how data can be represented using visual and tactile variables, as well as the affordances of VISMOCK. We demonstrate the generative power of our design space through four exemplars, created using VISMOCK. We use these exemplars to discuss the advantages and limitations of VISMOCK as a tool for data physicalization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it