Physicalization from Theory to Practice: Exploring Contemporary Challenges for Physicalization Design
Bibliographic record
Abstract
This workshop aims to delve into the evolving challenges of physicalization, drawing on prior research and workshops to explore overarching grand challenges in the field. Initially formalized within Human-Computer Interaction in 2015, `physicalization' involves encoding data into tangible forms. Despite significant progress in addressing initial challenges, new complexities emerge from the dynamic interplay of technology and human interaction. Building on insights from a prior CHI 2023 workshop, which focused on exemplar domain applications, our workshop aims to facilitate in-depth discussions on overarching grand challenges. Specifically, we focus on four key challenges: privacy, temporality, collaborative sensemaking, and sustainability of physicalization design. These focal points acknowledge the susceptibility of physicalizations to privacy concerns, collaborative interpretation, temporal usage, and sustainability considerations. Through interactive and collaborative activities, the workshop seeks to advance understanding and strategies for addressing these emerging challenges in the realm of physicalization, ultimately contributing to the advancement of the field.
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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 | 0.061 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.069 |
| Scholarly communication | 0.026 | 0.032 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.006 | 0.007 |
| 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; 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".