Exploring Feedforward in Data Physicalization Authoring Tools: Supporting Design Exploration Before Fabrication
Bibliographic record
Abstract
Unlike digital visualization, physicalization design introduces structural and material uncertainties, which require immediate feedback and often rapid iteration. This makes physicalization design a speculative, labor-intensive, and expertise-dependent activity. While speculative reasoning can be central to physicalization, authoring tools remain scarce and there is a lack of computational support that can help in anticipating physicalization outcomes. We explore how feedforward mechanisms (predictive features) might help physicalization designers anticipate post-fabrication qualities like tactile feel and expandability before fabrication, by iteratively developing and studying DataCuts, a physicalization authoring tool for designing Kirigami-based data physicalizations. Our study findings show that DataCuts’ simple feedforward functionalities can inform design decisions, shape people’s understanding of physical properties and encourage tactile representation over conventional mappings. Building on these results, we discuss implications for designing data physicalization support tools that integrate feedforward.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".