Studying design process and example use with Macaron, a web-based vibrotactile effect editor
Bibliographic record
Abstract
Examples are a critical part of any design process, but supporting their use for a haptic medium is nontrivial. Current libraries for vibrotactile (VT) effects provide neither insight into examples' construction nor capability for deconstruction and re-composition. To investigate the special requirements of example use for VT design, we studied designers as they used a web-based effect editor, Macaron, which we created as both an evaluation platform and a practical tool. We qualitatively characterized participants' design processes and observed two basic example uses: as a starting point or template for a design task, and as a learning method. We discuss how features supporting internal visibility and composition influenced these example uses, and articulate several implications for VT editing tools and libraries of VT examples. We conclude with future work, including plans to deploy Macaron online to examine examples and other aspects of VT design in situ.
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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".