Designing Interaction Categories for Kinesthetic Empathy
Bibliographic record
Abstract
Synchronous Objects is an interactive online dance work that allows audiences to look inside the choreographic structure of William Forsythe's One Flat Thing, reproduced (2000). Ohio State University, in collaboration with Forsythe, created twenty interactive visualizations or "objects" with varying levels of interactivity that present three main choreographic principles: alignments, cueing and movement material. This design allows users to empathize with the movement on multiple levels--shifting attention away from aesthetic biases and highlighting qualities of the movement that may otherwise be missed. We conducted a close reading analysis of the interaction design strategies employed within Synchronous Objects in order to understand what interaction design features support kinesthetic empathy. We suggest categorizing the objects into three types: instructional, exploratory, and translational and claim these interaction categories provide a useful framework for understanding how to design for kinesthetic empathy in computational models of movement. We discuss how these interaction categories represent movement through relatable images that a broader audience can appreciate and connect with. Our analysis contributes to research in movement and computation by including kinesthetic empathy as a design principle within movement representations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".