Tactile Narratives: Augmenting Body Maps through Textured Fabric in Soma Design
Bibliographic record
Abstract
In Human-Computer Interaction, body maps are a standard tool to understand an individual's bodily phenomenon. Body maps often use abstract drawings and text annotations on an outline of a body. However, little research has explored alternate ways we can collect similar data. In this pictorial, we present tactile body maps, which use an array of textured fabric circles attached to a felt-shaped body instead of a more traditional approach to drawing body maps. We first present an illustration of how researchers can use tactile body maps and show an example of the type of data collected in the method. We then tested the augmented body map method alongside drawing body maps and verbal-only body descriptions with eight participants to explore the benefits and disadvantages of each technique. Through the data, we present a set of considerations that a researcher can use to decide which way would be most appropriate for their soma design process.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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; both teacher heads agree on what is shown here.
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".